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EPA

Freight Demand Modeling and Logistics Planning for Assessment of Freight Systemsrsquo Environmental Impacts

PICo-PIs Tami C Bond Yanfeng Ouyang Christopher P L Barkan Bumsoo Lee

Students Taesung Hwang Liang Liu Sungwon Lee

March 5 2014

1 Background

2 Inter-regional Freight Demand Modeling

ndash Forecasting freight demand considering economic growth factors

ndash Freight transportation mode choice and its environmental impacts

ndash Freight shipment demand network assignment under congestion

Integrated decision-support software

3 Intra-regional Freight Demand Modeling

ndash Logistics systems planning for regional freight delivery

ndash Urban freight truck routing under stochastic congestion and emission considerations

4 Conclusions and Future Research Plan

Outline 1 Background










2

Background bull Rapid globalization and ever-increasing demand for freight movements bull Emission problems from freight transportation

ndash Most freight transportation modes are powered by diesel engines ndash Significant sources of national air pollutants (eg NOX PM) and greenhouse

gases (eg CO2) (ICF Consulting 2005)

bull Emissions from freight transportation activities ndash Climate change (on global scale) ndash Air quality and human health (in regional

and urban areas) bull Freight delivery systems need to be

thoroughly investigated to understand their impacts on environment

3

Emission projections today

4

Technology amp infrastructure

model Input-output

model Hybrid model

(+) response to economic environment

eg fuel switching (-) Little ldquoHow-tordquondash

engineering component

Economy-wide model

Separate economic sectors

Apply emission coefficient to activity

in each sector

Situation-specific

Data intensive requiring fleet composition traffic

links etc

Emissions from specific conditions amp vehicle types

hellipand tomorrow

Activity and growth driven by input-output

model

Linked to technology choice using general theoretical principles

Models (eg emission rates) constrained by

observations whenever possible

(+) Realistic emissions that can be connected

to policy decisions (-) Difficult to

extrapolate to other situations

5

Inter-regional freight

S Smith PNNL

B Lee S Lee Urban Planning

Y Ouyang T Hwang Transportation (CEE)

T Bond L Liu F Yan Air Quality (CEE)

For heavy-duty trucks and rail

6

Intra-regional freight

S Smith PNNL




Urban development scenarios

(compactpolycentricBAU)

For medium duty trucks

not presented here

Delivery activity Ring-sweep algorithm

Spatial autoregression

model

Air Quality and Climate

Impacts Models

Freight Transportation

Systems Models

Urban Spatial Structure and Input-Output

Models

Global Economic Forecasts Models

Inter-regional Freight Flow

Intra-regional Freight Flow

Various Economic Factors Projections

Regional Level Truck

Freight Delivery

Point-to-Point Truck

Routing in Urban Freight

Delivery Traffic Assignment

Mode Split

Trip Distribution

Employment Distribution

within Domestic Regions

Input-Output Commodity Value

Forecasts

Long-haul Truck and Rail Emission

Short-haul Truck Emission

Trip Generation

Air Quality and Climate Impacts in the US Regions

Global Air Quality and Climate Impacts

Framework

7

Scope (i) Inter-regional freight flow eg from Los

Angeles to Chicago (ii) Intra-regional freight flow eg within

Chicago metropolitan area (iii) Point-to-point delivery routing

Freight demand and logistics modeling Develop and integrate a set of US freight transportation system models to capture interdependencies on future economic growth and urban spatial structure changes

freight analysis zone (FAZ)

How much demand will be made

Where will the freight go Which modes will be

usedWhat routes will be used

Production

Attraction

Truck

Rail

Ship

Inter-regional Freight Demand

8

Trip Generation

Economic growth factor forecast for each

Trip Distribution

Entering and exiting freight demand (attractions and productions) by zone

Mode Split

Zonal OD freight demand

Traffic Assignment

Zonal OD freight demand by shipping mode

Traffic flow average speed on each link

Four-step freight commodity transportation demand forecasting model (NCHRP Report 606 2008)

123 domestic Freight Analysis Zone (FAZ)

geographical region

Introduction

bull Objective Forecast future freight demand that begins and ends in each FAZ and distribute them on all OD pairs

9

Trip Generation Trip Distribution Mode Split Traffic Assignment

bull Methodology RAS algorithm (Stone 1961 Stone and Brown 1962) Basic Ideas ndash Forecast of economic growth factors are given for all FAZs ndash Current FAZ structure does not change (ie neither new zone will appear nor

currently existing zone will disappear) ndash Distribution of future freight demand is proportional to that of base-year demand

Freight Demand GenerationDistribution

10

bull Structure of base-year freight demand distribution data

Origin zone Destination zone o O

i y o

i oP

i dA i y

di odD

d D= base-year total production of commodity i

in an origin zone o = base-year total attraction of commodity i in

a destination zone d = growth rate of commodity i production in

an origin zone o for future year y = growth rate of commodity i attraction in a

destination zone d for future year y = freight volume of commodity i moving

from origin zone o to destination zone d

i dA

i oP

i y o

i yd

i odD

For commodity type isin ሼ1 2 hellip ሽ

= origin zone set 1 2 hellip Z = destination zone set 1 2 hellip ZD

O

Let be base-year commodity i freight movement from origin o to destination d

11

Step 0 Generate base-year freight demand OD matrix for commodity i

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

Step 1 Estimate future production and future attraction for all FAZs

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW

Multiply each by and each

by Define

i oP i y

o i y d

idA

i i y i i i y i o o o d d dV P W A o O d D

i odD


bull Then

12

Step 2 Since future input and output commodity growth are modeled separately

Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W

Total future production summed across all

origin zones

Total future attraction summed across all destination zones

ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW

bull Assume freight commodity productions are derived by attractions

bull Multiply future productions of all origin zones by the same factor


i o

o O V

i

d d D

W =

13

Step 3 Apply RAS algorithm Modify each entry iteratively to match with the future production in each row and the future attraction in each column

i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW


Define tolerance 1 and let large positive integer and 1

Define and

While ( ) and ( 1 for some or 1 for some )

Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14


Freight Transportation Mode Choice

bull Goal Draw connections among various economic and engineering factors freight transportation modal choice and subsequently freight transportation emissions

ndash Significant difference in emissions across modes

Source EPA (2008)

CO2 Emission Factor (kgCO2ton-mile) CH4 Emission Factor (gCH4ton-mile) N2O Emission Factor (gN2Oton-mile) On-Road Truck 02970 00035 00027

Rail 00252 00020 00006 Waterborne Craft 00480 00041 00014

Aircraft 15270 00417 00479

Ref Hwang TS and Ouyang Y (2014) ldquoFreight shipment modal split and its environmental impacts An exploratory studyrdquo Journal of the Air amp Waste Management Association 64(1) 2-12


bull Focus on two dominating freight modes Truck and Rail bull Macroscopic binomial logit market share model for mode choice

o Dependent variable Annual market share of shipments between modes (between 0 and 1)

o Explanatory variables for each commodity type Commodity value per ton ($ton) VALUE Avg shipment distance for truck (mile) DISTT

Avg shipment distance for rail (mile) DISTR

Crude oil price ($barrel) OILPRC o Data Observed modal split for each OD pair

15

Mode Choice Binomial Logit Market Share Model

Utility of truck for commidity n U n a b VALUE c DIST d OILPRC T 1n 1n 1n T 1n

Utility of rail for commidity n U n a b VALUE c DIST d OILPRC R 2n 2n 2n R 2n n n nU U UT T R

n e eMarket share of truck for commidity n P T n n n nU U U UT R T Re e e 1 nUR

n e 1Market share of rail for commidity n P R n n n nU U U UT R T Re e e 1 n PT n nln U U n T R1 P T

( OILP RC (a a ) (b b ) VALUE (c ) DIST c ) DIST (d d ) 1n 2n 1n 2n 1n T 2n R 1n 2n

bull Generalized linear form with four explanatory variables bull Intercept and coefficients estimated via linear regression

16

Data Sources and Processing

bull Freight Transportation Data ndash Freight Analysis Framework (FAF) database from the US DOT

Datasets Version 2 (FAF2) for year 2002 and version 3 (FAF3) for year 2007 ndash Commodity Flow Survey (CFS) data from the US Census Bureau

Freight transportation activities in years 1993 and 1997

Average shipment distances of truck and rail ndash West Texas Intermediate (WTI) crude oil price from Economagiccom o Merged into one useable database (69477 observations)

bull Divide the database into two sets for each commodity type i Training set for estimation 23 of the total observations ii Test set for validation 13 of the total observations

bull Statistical software package R (version 2121)

17

Estimation Results and Goodness of Fit

Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 8 Type 9 Type 10 Estimate 1989E+00 1777E+00 3800E+00 9383E-01 1390E+00 2954E+00 3014E+00 1910E+00 1702E+00 9978E-01 z -statistic Intercept 1276100 586829 2833500 1035700 835080 1568500 2113920 417690 547290 81140 P r(gt|z |) lt0001 lt0001 lt0001 lt0001 lt0001 lt0001 lt0001 lt0001 lt0001 lt0001 Estimate z -statistic P r(gt|z |)

Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001

Estimate z -statistic P r(gt|z |)

(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001


WTI c rude oil price

4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001

(b) Number of data used 3802 5468 3753 3105 5883 6068 6035 5100 5041 2062 (c) Pseudo McFa dden 0348 0427 0241 0659 0270 0381 0133 0203 0134 0438 R-squared Nagelkerke 0391 0456 0261 0747 0311 0410 0143 0229 0143 0445


bull All estimates are statistically significant (all p-values le 0001)

bull Interpretations and insights ndash Positive Intercept Everything else being equal truck is more likely to be chosen ndash Positive ldquoValue per tonrdquo Truck tends to ship higher value goods than rail ndash Negative ldquoAvg truck distancerdquo As shipping distance increases utility of truck

decreases ndash Negative ldquoAvg rail distancerdquo As shipping distance increases rail is preferred ndash Negative ldquoWTI crude oil pricerdquo As oil price increases rail is preferred

19

20

Traffic Assignment

bull Goal Assign freight traffic onto modal networks for all shipment OD pairs

bull Route choice rule User equilibrium (Wardrope 1959 Sheffi 1985) ndash Each motorist selects the shortest travel time route between OD ndash All used routes connecting each OD pair have the same costtravel time which is

less than or equal to the costs of unused routes

bull Algorithm 1 Convex combinations algorithm (Frank and Wolfe 1956 coded in VC++) 2 Input graph representation of modal networks demand for all OD pairs 3 Output file assigned traffic flow average speed on each link link cost etc


Truck Traffic Assignment bull Model development

ndash Standard network assignment problem under user equilibrium principle (Sheffi 1985)

ndash Bureau of Public Roads (BPR) link cost function (Bureau of Public Roads 1970) modified to include background traffic volume

bull Data for graph representation of freight truck network 1) OD nodes 120 centroids of FAF3 regions boundary

ndash Exclude Hawaii (2 zones) and Alaska (1 zone) 2) US road network FAF3 network

ndash Consider only major interstate highways ndash Background traffic (AADT) and link capacity in Year 2007

bull Data for truck freight demand FAF3 truck shipment database (FHWA US DOT 2011)

ndash Real truck freight demand data (in tonnage) in Year 2007

21

22

Truck Traffic Assignment Data (2)

FAF3 zones Major Interstate Highway

1) 178 nodes ndash Centroids of domestic FAZs (120 nodes) ndash Major junctions in the interstate highway

network (58 nodes)

2) 14400 OD pairs ndash Each centroid of 120 FAF3 zones is both

origin and destination of freight demand

3) 588 links ndash Mostly major interstate highways ndash Some local roads for FAF3 centroids

located far from the major interstate highway network

bull Simplified US major highway freight truck road network

Truck Traffic Assignment Data (3) bull Parameters

- Average truckload (tons per truck) = 16 (FHWA US DOT 2007 EPA and NHTSA 2011)

- Passenger-car equivalents (assuming rolling terrain) = 25 (HCM 2000) - Hours of operation of the freight truck delivery system = 24ൈ365 - Truck free flow speed (mph) = 65 (Bai et al 2011) - Background traffic = AADT(2times24) - BPR link cost function modified to include background traffic volume

b

t( ) t f 1 C

where t f link free flow travel time (hr) assigned traffic volume of vehhr b background traffic volume of vehhr C link capacity of vehhr 015 and 4

23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow

Truck Traffic Assignment Results

24

bull Total Cost = sum (Link Travel TimetimesAssigned Link Flow) = = 69982788 (veh-hrhour) bull Convergence is reached within a tolerance of 00001 after 12 iterations (0640 sec CPU time) bull Output link and node number link distance total and assigned traffic volume link cost (link travel

time) average link speed at equilibrium

a a a a A

t x x

FAF3 zones le Assigned flow le 0 lt Assigned flow le 00 lt Assigned flow le 00 lt Assigned flow le

0 lt Assigned flow le


Unit of assigned flow of vehicles (passenger cars) per hour

500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0

Model Validation bull Freight traffic distribution (annual tonnage) on the US highway (red) rail (brown)

and inland waterways (blue) networks in Year 2007 (FHWA US DOT 2011)

25

Model Validation

bull Truck traffic distribution on the US highway network

Trend consistent in a high level Washington Oregon California Florida the Midwest states near Chicago and northeastern regions

Less emphasized in our result Some main highway links that connect Southern California Arizona and Oklahoma

26

27

Rail Traffic Assignment bull Rail network operates very differently from highway network

- Link traffic flow in opposite directions shares the same track infrastructure - Assign bi-directional traffic flow on one shared undirected link (ie undirected graph)

bull Railroad-specific link cost function (Krueger 1999 Lai and Barkan 2009) ndash For undirected railroad link

100

e ee e e e e

dt T e e E

= link free flow travel time (hour) = link length (mile) = the total rail link flow ( of trainsday) = parameters uniquely determined by rail operating conditions

eT

ed

e

e e

e E

where

Ref Hwang TS and Ouyang Y ldquoAssignment of freight shipment demand in congested rail networksrdquo Transportation Research Record In press

i j xji

xij

xji (+ xij) i j

Rail Traffic Assignment Methodology

bull Equivalent directed graph representation of the undirected rail network Each undirected link is replaced by two separate directed links in opposite directions

xij (+ xji)

bull Railroad link cost function for the directed graph ij dij ij xij x ji x T e i j At x ( )ij ij ji ij 100

Link travel times on both directed links (from node i to j and from node j to i) are identical

bull Modify conventional convex combinations algorithm - Consider traffic volume in both directions whenever link cost is updated

28

Rail Traffic Assignment Data (1) bull Data for graph representation of rail network

1) OD nodes 120 centroids of FAF3 regions boundary ndash Exclude Hawaii (2 zones) and Alaska (1 zone)

2) US rail network Rail network GIS data (ATLAS 2011) ndash Select rail network main lines on which Class І railroads (AMTK BNSF CSXT

KCS NS UP CN CP in the database) operate ndash Incorporated double track information obtained from Richards and Cobb (2010)

bull Data for rail freight demand ndash FAF3 rail shipment database (FHWA US DOT 2011) freight demand in 2007 ndash Converted the freight shipment demand in tonnage into equivalent numbers of

trainloads based on the types of commodities (AAR 2007 Cambridge Systematics Inc 2007)

bull Parameters operation days per year = 365 free flow speed (mph) = 60 (Krueger 1999)

29

Railroad Traffic Assignment Data (2) bull Simplified US rail network

1) 183 nodes ndash Centroids of domestic FAZs (120 nodes) ndash Major intersections in the selected rail

network (63 nodes)

2) 40909 OD pairs ndash Consider both shipment OD pairs and

commodity types

3) 566 links ndash Mostly major railroad tracks on which Class І

railroads operate FAF3 zones ndash Some tracks on which other minor railroads Selected Railroad Track operate for FAF3 centroids located far from

the major rail network

30

Railroad Traffic Assignment User Equilibrium Results

FAF3 zones 0 le Assigned flow le 10

10 lt Assigned flow le 20





60 lt Assigned flow

Unit of assigned flow of trains per day

t x x x ij ij ji ij bull Total Cost = sum (Link Travel TimetimesAssigned Link Flow) = ( ) i j A = 75426 (train-hrday) bull Convergence is reached within a tolerance of 0001 after 2569 iterations (25559 sec CPU time) bull Output link number link origin and destination node link distance freight shipment volume (for

each commodity type) link cost (link travel time) average link speed

31

Model Validation bull Rail traffic distribution on the US rail network

Trend consistent at a high level Washington California Wyoming Montana the Midwest states near Chicago northeastern regions and some main links that connect Southern California Texas and Kansas

More emphasized in our result Idaho Oregon and southeastern regions

32

Software Development

33

bull Integrated decision-support software for four-step inter-regional freight demand forecasting

bull Visual Basic Applications (VBA) in Microsoft Excel platform

Overview of the software

Input

Main Program

Output

Included in one Excel file

bull Procedure of the program

34


Input

Main Program

Output

35

bull Input worksheets - Each step in the four-step analysis requires different

input worksheets to conduct the analysis - Total eighteen different input worksheets

Trip generation and Trip distribution ldquoAttraction_S1rdquo ldquoAttraction_S2rdquo ldquoAttraction_S3rdquo ldquoAttraction_S4rdquo ldquoProduction_S1rdquo ldquoProduction_S2rdquo ldquoProduction_S3rdquo ldquoProduction_S4rdquo and ldquo2007Demandrdquo

Modal split ldquoTruckDistrdquo ldquoRailDistrdquo and ldquoModalSplitrdquo

Network assignment ldquoTruckDemandrdquo ldquoRailDemandrdquo ldquoTruckNetworkrdquo ldquoRailNetworkrdquo ldquoTruckNoderdquo and ldquoRailNoderdquo



Input

Main Program

Output

36



Input

Main Program

Output

bull Output worksheets Results from different steps will be recorded in seven different output worksheets - Trip generation

ldquoTrip_Generationrdquo - Trip distribution

ldquoTrip_Distributionrdquo - Modal split

ldquoModal_Splitrdquo - Truck freight demand network assignment

ldquoTruckResultrdquo and ldquoTruckMaprdquo - Rail freight demand network assignment

ldquoRailResultrdquo and ldquoRailMaprdquo

Software Development Visualization of the final results

ldquoTruckMaprdquo worksheet ldquoRailMaprdquo worksheet

bull Help decision-makers explore atmospheric impacts of future freight shipment activities in various economic scenarios

37

Illustrative Examples of Model Application

Sample Questions bull How would economic growth affect inter-regional

freight transportation bull How would fuel price affect freight modal choice

bull How could congestion in current transportation infrastructure restrict freight movements and what are the impacts of capacity investments

38

Future Freight Demand Forecast bull Forecast future freight demand distribution within the US from 2010 to

2050 in five-year increments bull Four scenarios

ndash Scenario 1 (S1) High GDP growth amp Business as usual ndash Scenario 2 (S2) High GDP growth amp Climate policy ndash Scenario 3 (S3) Low GDP growth amp Business as usual ndash Scenario 4 (S4) Low GDP growth amp Climate policy

bull Data 1 Base-year freight demand distribution matrix

Freight Analysis Framework data version 3 (FAF3) for Year 2007 Origin Destination Commodity type Freight demand (in tonnage)

2 Future IO commodity value growth estimates for all scenarios Exogenously given from the input-out model (2005-2050 in five-year increments)

39

Future Freight Demand Forecast

40

bull Freight demand forecasting results ndash Algorithm converged in a short time ndash Future freight demand is generated (360 120-by-120 matrices)

(b) Year (c) Total freight demand

forecasted (thousand ton) (g) change

(d) Total freight demand forecasted (thousand ton)

(h) change (e) Total freight demand

forecasted (thousand ton) (i) change

(f) Total freight demand forecasted (thousand ton)

(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645

(a) Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4

High GDP growth with business as usual High GDP growth with climate policy Low GDP growth with business as usual Low GDP growth with climate policy

bull Suitable for long-term economic forecasts Global economic forecasts models hard to capture unexpected short-term economic fluctuations (eg recession in 2007-2009)

Model Application - Emission Estimation Modal split and the following emission estimations for a range of WTI crude

oil price bull Select one arbitrary data record Commodity type 5 (basic chemicals chemical and

pharmaceutical products) from Texas to Colorado

Total annual freight shipment demand in data = 328000 ton bull Forecast annual freight shipment split for different oil price range bull Estimate total emission and greenhouse gas inventory

Emission factors adopted from EPA (2008) and NRDC (2012)

41

Freight value per unit weight Avg truck distance Avg rail distance

$124085ton 1005 miles 1332 miles

CO2 emission factor (kgCO2ton-mile)

CH4 emission factor (gCH4ton-mile)

N2O emission factor (gN2Oton-mile)

PM10 emission factor (gPM10ton-mile)

Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013

Model Application - Emission Estimation

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o) WTI crude Truck share Rail share Truck CO2 Rail CO2 Total CO2 Truck CH4 Rail CH4 Total CH4 Truck N2O Rail N2O Total N2O Truck PM10 Rail PM10 Total PM10

oil price prediction prediction emission emission emission emission emission emission emission emission emission emission emission emission ($barrel) () () (ton) (ton) (ton) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg)

40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529

bull National emission estimation Aggregate emission calculations across all shipment OD pairs and all commodity types

42

Rail Network Capacity Expansion and Its Effect on Network Assignment

bull Rail freight demand projected to increase 88 by Year 2035 - Sever congestion is expected (Cambridge Systematics Inc 2007) - Infrastructure investment may be needed near potential chokepoints rarr Will affect future rail freight demand assignment patterns

bull ldquoBefore and Afterrdquo comparison for Year 2035 - Action on the most congested railroad links in 2035 rarr Average link speed le 10 mph

- Single tracks will be expanded to full double tracks

43


44

Our model Cambridge Systematics Inc (2007)

FAF3 zones 50 lt Avg speed 40 lt Avg speed le 50 30 lt Avg speed le 40 20 lt Avg speed le 30 10 lt Avg speed le 20

0 lt Avg speed le 10

Unit of Average speed mph

bull Congestion prediction in Year 2035 without infrastructure investment


Congestion prediction from our model in 2035 after capacity expansion




(a) Capacity expansion Before After reduction

(b) Total cost (103 train-hrday) 2025 1364 3267

(c) Total ton-mile (103 ton-mileday) 10496597 10411213 081

bull Decrease in total ton-miles - Less detour toward shipment destinations - Improvements in rail freight delivery efficiency

45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46





Introduction bull Bulk of freight arriving at the destinations (ie terminals) in each FAZ

- Broken for delivery to distributed individual customers - Also freight needs to be collected from a large number of supply points to the

set of origins (ie terminals) in each FAZ

bull Freight delivery activities within large urban areas are critical issues - Emissions from freight shipments comprise a large share of toxic air pollutants

in most metropolitan areas worldwide (OECD 2003) - Residents in metropolitan areas are more likely to be affected by the air

pollution problems than those in rural areas

bull Need to investigate freight shipment modeling and logistics planning at the intra-regional level

47

Introduction bull Logistics systems model for freight distribution within an FAZ

48

Total travel distance within a delivery region = Total line-haul distance + Total local travel distance

(Ouyang 2007)

ndash Vehicles need to serve spatially distributed customer demand which might be large scale (Large-scale Vehicle Routing Problem)

ndash Estimate network delivery efficiency

bull Methodology Continuum Approximation (Newell and Daganzo 1986a) (i) Assume continuous customer demand density that may vary

slowly over space (ii) Suitable for large-scale estimation (asymptotic approximation)

bull Objective Estimate near-optimum total delivery distance Possible zoning and

delivery plan example

Within FAZ Delivery Procedure

49

Each FAZ is composed of a set of mutually disjointed census tracts

bull Assumptions ndash Freight demand in each census tract is concentrated at the centroid of the census tract ndash Freight demand will be assigned to the nearest terminal (if multiple terminals) ndash Freight is delivered by identical short-haul trucks with constant low speed (eg 30 mph) ndash Euclidean metric roadway network

bull Objective Estimate the total transportation cost (ie total travel distance)

bull Application of the ring-sweep algorithm to estimate regional freight delivery

Freight delivery region (ie FAZ)

Within FAZ Delivery

50

di = distance from the terminal to the centroid of the census tract i Eij = number of employees in an industry type j in the census tract i I = total number of census tracts J = total number of industry types considered C = truck capacity (in tonnage) D = total freight demand in a given FAZ (tons per day)

1 1 1

1 1

2 Total line-haul distance ( ) =

I J ij ii j

I J iji j

D E d L

C E

(1) Total line-haul distance

bull Total delivery cost to serve freight demand within an FAZ = Total line-haul distance (L1) + Total local travel distance (L2)

bull ldquodistributionrdquo and ldquocollectionrdquo

Within FAZ Delivery (2) Total local travel distance

N = total number of demand points in a given FAZ I Nii1

where Ni = total number of demand points in each census tract i J Eij j1 aj

aj = average number of employees per firm in an industry type j - represents how many employees are served on average by one

truck visit - may vary across industries

N

Aδ = uniformly distributed demand point density in a given FAZ

where A = area of an FAZ

057NTotal local travel distance ( L2 ) =

51

Application bull Estimate regional freight delivery cost and the related emissions (CO2 NOX PM and VOC) in

36 FAZs that cover 27 major Metropolitan Statistical Areas (MSAs) from 2010 to 2050 bull Data

(i) Forecast of employment distributions (from urban spatial structure model) wholesale trade retail trade and manufacturing industries

(ii) Future truck and rail freight demand for each FAZ (from four-step inter-regional freight demand model)

bull Three urban development scenarios (i) Scenario 1 ldquoBusiness as usualrdquo current urban sprawl continues in the US (ii) Scenario 2 ldquoPolycentric developmentrdquo CBD (current trend) sub-centers (high-growth) (iii)Scenario 3 ldquoCompact developmentrdquo both CBD and sub-centers (high-growth)

bull Inter-regional freight demand scenario high GDP growth under business as usual bull Freight collection and distribution deliveries from truck and railroad terminals are modeled

separately bull Commodities are delivered separately considering different industry types bull Light and medium trucks capacity = 4 tons (FHWA US DOT 2007 Davis et al 2012) avg

speed = 30 mph

52

Application bull Regional freight delivery from truck terminals

A number of truck terminals are located near the junctions of major highways

1 Commodities related to the wholesale and retail trade industries for terminal k Ik 2 k k2 D D E d 057N I 2 N1 W R ij ki k f k 1 k k fk i1 j1 L = where N f

a Eij and f ALf 1 = I 2 f 2 k i1 j1 C Eij

f 1 k i1 j1

2 Commodities related to the manufacturing industry for terminal k Ik k k2 D E d k 057N p k 2 Ik k N p2 M i3 ki Lk = i1 Lp2 = where N E and p i3 pp1 I k i1 2 kC Ei3 p

a A i1

3 Total freight delivery cost in the FAZ summed across all truck terminals k K

k k k kG K L L L L T f 1 f 2 p1 p2k1

53

Application bull Regional freight delivery from railroad terminals

Several railroad terminals are located near the intersections of major railroad links 1 Commodities for direct shipments from railroad terminals

- Trucks are not involved in freight delivery 2 Commodities for short-haul truck delivery from railroad terminals

(1) Commodities related to the wholesale and retail trade industries for terminal q q2 D D I 2 E d q q

1 W R ij qi 057N I 2 Nq i1 j1 q s q 1 q q sL = L = where Ns a Eij and s As1 I 2 s2 q i1 j1C Eij 1 qi1 j1 s

(2) Commodities related to the manufacturing industry for terminal q Iq q q2 D E d q 057Nm q 2 Iq q NmLq

m1 = 2 M i1 i3 qi Lm2 = where N E and m i3 mi1a AC I E m q

2 hi3i1

3 Total freight delivery cost in the FAZ summed across all railroad terminals q QQ q q h hGR q1Ls1 Ls2 Lm1 Lm2

54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York

P M (kg per day) VOC (kg per day)

Los Angeles

San Francisco

MSA Scenario CO2 (103 kg per day) NOX (kg pe r da y)

2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564

of FAZ MSA Scenario Freight shipment (103 ton-mile)

Case Study

55Emission factors (TRL 1999)

Scenarios 1 Business as

usual 2 Polycentric

development 3 Compact

development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56





Introduction bull Improvements in fleet operations from trucking service sector

ndash Reduction in vehicle emissions ndash Huge benefits (urban air quality human exposure)

bull Roadway congestion in large urban areas is stochastic ndash Real time information technology

Avoid heavy congestion by dynamically choosing the minimum expected cost path ndash Shortest path problem in a stochastic network setting (Miller-Hooks and

Mahmassani 2000 Waller and Ziliaskopoulos 2002) Cost component travel delay (focus on minimizing the expected total travel time)

57

Introduction

58

bull ldquoTotalrdquo cost = sum [ Total delivery time + Emissions + Penalty ] bull Minimum expected travel time solution (classical shortest path approach)

ndash Does not necessarily guarantee the minimum expected ldquototalrdquo cost solution

bull Traffic congestion in large urban areas ndash Responsible for air pollution and related human health problems (Copeland 2011)

bull Trucking freight delivery contribute to the largest share of air pollutants in metropolitan areas (ICF Consulting 2005)

ndash Environmental cost caused by truck activities (CO2 VOC NOX and PM) ndash Penalties for late or early truck arrival at destination (ensure delivery punctuality)

Model Formulation bull Consider urban roadway networks

Represented by a graph D(V A) where V = node set and A = directed link set bull From origin to destination truck driver needs to decide the next link

whenever heshe arrives at each node to minimize the expected total cost bull Assumptions

(i) Truck speed on each link is stochastic (uniquely determined by stochastic congestion state on the link)

(ii) Truck speed on each link follows a certain probability distribution - Fixed throughout the period of routing study (eg morning rush hour) - Not necessarily identical across the links

(iii) Consider only major arterial roads or freeways to represent urban network links - Queue formed on a link does not spill over into immediate downstream links - Congestion states are independent across the links

59

Model Formulation isin destination s isingorigin

(mile) ܣ ሻ isin ሺ = length of linkijd ሻ isin ሺ = stochastic truck speed (mph) on link ijU ܣ

W(middot) = emission rate (gveh-mile) function of the truck speed Uij

ሻ isin ሺ = assignment of vehicle on link ijx ܣ

Total travel time Emissions Penalty

d ij ij ij ij ij

i j i j Uij

1 if i g

( ) A Uij ( )A ( ) i j A d x W U Minimize xij P ij x

d Minimizes expected total travel cost

Flow conservations at all subject to xij x ji

1 if i s network nodes |( ) j j i j i j A |( ) A 0 otherwise

xij ( i j) Binary decision variables01 A

($hr) ($gram) parameters to convert units P( ) penalty ($) for late or early arrival function of the total travel time ( ) T

E scheduled travel time (hr) shippersrsquo preference on the total delivery time (given) If T E no penalty otherwise assign penalt y

60

Solution Approaches (1) Dynamic Programming

bull Stage each node i V in a given network bull State truck arrival time m [0 ) at each stage i V bull Decision choice from a finite set of decisions on the next link to move

onto ( i j i j) | ( ) A bull Truck speed positive continuous random variable which follows a certain

probability density function bull Algorithm can be written into a recursive Bellman equation with backward

induction bull Optimal solution

Minimum expected total cost of the freight truck from its origin

61

Solution Approaches (2) Deterministic Shortest Path Heuristic

bull In many real roadway networks truck drivers need to select the next travel link in real time (ie within several seconds)

bull Heuristic to find - Feasible solution in a very short computation time even for very large networks - Upper bound to the optimum solution

bull Shortest path from origin to destination is obtained using the expected link cost considering only link travel time and the related emissions

bull Once truck reaches the destination penalty cost is added

62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20

Numerical Examples bull Tested on four examples small networks and large-scale urban transportation networks

63

5-node and 13-link network (Powell 2011) 15-node and 25-link network (Papadimitriou and Steiglitz 1998)

24-node and 76-link Sioux Falls network (Bar-Gera 2009) 416-node and 914-link Anaheim network (Bar-Gera 2009)

Origin

Destination

Origin Destination

Origin Destination

Numerical Examples bull Assign a high penalty for late but a low penalty for early arrival

100( ) if T E T E where T = total travel time (hr) ( ) P T 10(T E ) otherwise E = scheduled travel time (hr)

bull Truck emission rate functions (gveh-mile) for CO2 VOC NOX PM (TRL 1999)

bull Parameters that convert weight of emissions and time into monetary values 280 ($tonCO2) 200 ($tonVOC) 200 ($tonNOX) 300 ($tonPM10) (Muller and Mendelsohn 2007 Winebrake et al 2008) 20 ($hr) (Bai et al 2011)

bull Truck speed on each link follows a randomly generated log-normal distribution

mean = uniform [20 60] (mph) sd = uniform [10 15] (mph)

64

Numerical Examples Computational Results

(c) Min expected (d) Gap (e) Solution time total cost ($) () (sec)

3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network

5-node and 7-link network


24-node and 76-link Sioux Falls network

(b) Algorithm

Shortest path heuristic Dynamic programming

(D = 0025) Shortest path heuristic Dynamic programming



(D = 0040)

254

416-node and 914-link Anaheim network

2049 261 0009

0725-1997

4955 282

-4819

2102

-

65

(a) Network (b) Scenario (c) Min expected

total cost ($) (d) Travel time ($) (e) Emissions ($) (f) Penalty ($)

Benchmark design 3545 1388 1376 780 P roposed approach 3251 1439 1081 730

294 -051 295 050 829 -367 2142 641

Benchmark design 2122 635 590 896 Proposed approach 1997 673 446 878

125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176

416-node and 914-link Anaheim

network Cost difference


Cost difference


Cost difference

24-node and 76-link Sioux Falls



66

bull Benchmark routing = Ignoring emission cost in selecting the route bull Proposed routing = Considering emission cost in selecting the route bull Cost difference = Cost from the benchmark routing - Cost from the proposed routing

SPEW-Trend fleet model

Represent how emissions are affected by technology change and modal choice

67

CO2 emission projection

Climate policy (carbon tax) causes modal shift to railwayndash BUT not ldquoenoughrdquo (Commodity-limited)

68

Fuel use projection

69

No congestion (by tonne-km only)

With projected eff improvement

With congestion

GCAM (input-output)

( regression)

Heavy-duty vehicle fuel use

Emission projection ndash air pollutants (congestion case)

70

Conclusions

bull Environmental problems from freight shipment activities ndash Climate change (on global scale) ndash Air quality and human health (in regional and urban areas)

bull Choice of freight mode and routing them betweenwithin geographical regions significantly affect regional and urban air quality

bull Freight demand models are developed to reflect dependences on future economic growth and urban spatial changes

bull Scope of the freight transportation ndash Inter-regional freight flow Four-step freight demand forecasting model ndash Intra-regional freight flow Various network optimization models and solution

approaches

71

Contributions bull In this interdisciplinary project we

ndash Develop a comprehensive freight shipment modeling framework ranging from initial collecting systems to freight movements and routing at the national scale and then to final distributing systems

ndash Provide deeper understanding of the interdependencies and connections among multiple traditionally separated research fields

ndash Aid decision-makers in evaluating freight handling decisions that contribute to reducing adverse impacts on air quality and climate change

ndash Facilitate decision-making processes in the freight industries or the government agencies by providing an integrated decision-support software

ndash Extend and apply to other studies such as transportation network capacity expansion and maintenance as well as traffic safety prediction

ndash Enhance human health and social welfare

72

Future Research

(1) Trip generation and trip distribution bull ldquoDistribution of future freight demand is proportional to that of the

base-year freight demandrdquo can be relaxed

rarr Gravity model for freight demand distribution bull Once newer version of FAF database becomes available rarr More recent base-year to improve forecast accuracy

(2) Modal split bull Update the models using additionalnewer version freight demand data rarr Estimation of precise environmental impacts of freight transportation

systems

73

Future Research (3) Network assignment

bull Impacts of infrastructure investment in the rail network on modal split rarr Enhanced level of service and its effect on future rail freight demand (ie

against other modes in a competitive freight shipment market)

(4) Stochastic urban freight truck routing problem bull Apply time-dependent stochastic congestion state on each link rarr Link travel time and following emissions will be affected by stochastic

truck speed as well as truck arrival time at the link origin node bull Include local and collector roads in the urban transportation networks rarr Truck speed on downstream and upstream links may be correlated

bull Apply environmental impacts from transportation activities to other stochastic network optimization problems

74

Thank you Any questions

75

Background bull Freight Analysis Zone (FAZ)

ndash Defined in Freight Analysis Framework to represent the US geographical regions with regard to freight activities

ndash Composed of 123 domestic regions in total bull 74 metropolitan areas bull 33 regions representing the remaining parts of the states that these 74

metropolitan areas belong to bull 16 remaining regions each of which represents an entire state

bull Map of domestic FAZs in Freight Analysis Framework version 3

76

12345678910

10 Commodity Types

77

Commodity type Commodity description Agriculture products and fish

Grain alcohol and tobacco products Stones nonmetallic minerals and metallic ores

Coal and petroleum products Basic chemicals chemical and pharmaceutical products

Logs wood products and textile and leather Base metal and machinery

Electronic motorized vehicles and precision instruments Furniture mixed freight and miscellaneous manufactured products

Commodity unknown

1 Background










Outline 1 Background










2







3


4


model Input-output

model Hybrid model




Economy-wide model



in each sector

Situation-specific


links etc


hellipand tomorrow


model







5


S Smith PNNL





6


S Smith PNNL







not presented here



model


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







3


4


model Input-output

model Hybrid model




Economy-wide model



in each sector

Situation-specific


links etc


hellipand tomorrow


model







5


S Smith PNNL





6


S Smith PNNL







not presented here



model


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


4


model Input-output

model Hybrid model




Economy-wide model



in each sector

Situation-specific


links etc


hellipand tomorrow


model







5


S Smith PNNL





6


S Smith PNNL







not presented here



model


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

5


S Smith PNNL





6


S Smith PNNL







not presented here



model


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

6


S Smith PNNL







not presented here



model


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

7









Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown





Production

Attraction

Truck

Rail

Ship


8

Trip Generation


Trip Distribution


Mode Split


Traffic Assignment





geographical region

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Introduction


9





10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


10



i y o

i oP

i dA i y

di odD







i dA

i oP

i y o

i yd

i odD



O


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


11


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction


1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

O D

i odD i

oP

idA

i oP

idA

i oV

i dW


by Define

i oP i y

o i y d

idA


i odD


bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

bull Then

12


Update

i d

i i d D o o i

o o O

W V V o O

V

i o

o O V

i

d d D

W


origin zones


ne

1 2 d Z Given

P roduction Future

P roduction

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD i

oP

i dA

i oV

i dW




i o

o O V

i

d d D

W =

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

13


i odD

1 2 d Z Given

P roduction Future

Production

1

2

o

Z

Given Attraction

Future Attraction

O D

i odD

idA

i oP i

oV

i dW



Define and


Set

U

i i i io d o di i

od od d D o O

i i o d

i i i od o od

L n V WR o O C d D

D D

n L R o O C d D

D R D o O d D

pdate

Set

Update

Update 1

i i d d i

od o O

i i i od d od

i i o o i

od d D

WC d DD

D C D o O d D

VR o OD

n n

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

14





Source EPA (2008)



Aircraft 15270 00417 00479








15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







15








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








16








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








17



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



Value per ton

2428E-03 859300 lt0001

2096E-03 712443 lt0001

1059E-03 121100 lt0001

9746E-03 2538900

lt0001

6210E-04 728940 lt0001

6130E-04 523840 lt0001

4850E-04 459340 lt0001

1113E-04 194840 lt0001

7085E-04 365500 lt0001

4311E-03 154580 lt0001


(a) Estimation

results

Avg truck distance

-1532E-03 -279600

lt0001

-1766E-03 -168074

lt0001

-1190E-03 -248800

lt0001

-1663E-03 -339000

lt0001

-1531E-03 -241820

lt0001

1904E-04 25200 lt0001

-3142E-03 -371460

lt0001

-4025E-03 -211300

lt0001

-1901E-03 -179230

lt0001

-2042E-03 -47210 lt0001


Avg rail distance

-1123E-03 -225800

lt0001

5149E-06 530

lt0001

-1960E-03 -495800

lt0001

-2155E-03 -501900

lt0001

2780E-04 48540 lt0001

-2026E-03 -291250

lt0001

1225E-03 161370 lt0001

2580E-03 149490 lt0001

2232E-04 23450 lt0001

-1599E-03 -13870 lt0001



4579E-03 163400 lt0001

-4808E-03 -96559 lt0001

-1383E-02 -599300

lt0001

-2901E-02 -1466900

lt0001

-7312E-03 -275890

lt0001

-3134E-03 -81830 lt0001

-1297E-03 -38990 lt0001

1011E-02 96390 lt0001

2285E-02 494840 lt0001

3305E-02 43210 lt0001






19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown





19

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

20

Traffic Assignment














21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown









21

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

22




network (58 nodes)









b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown




b

t( ) t f 1 C


23

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

0 60060 120012 180018 2400240 3000

3600360

0 lt Assigned flow


24



a a a a A

t x x





500 1000

1500 2000 2500 3000

3000 2500

2000 1500 1000 500 0



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



25

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Model Validation




26

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

27




100

e ee e e e e

dt T e e E


eT

ed

e

e e

e E

where


i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

i j xji

xij

xji (+ xij) i j



xij (+ xji)




28








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








29



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



network (63 nodes)


commodity types




30








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








60 lt Assigned flow




31




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown




32


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


33




Input

Main Program

Output



34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


34


Input

Main Program

Output

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

35








Input

Main Program

Output

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

36



Input

Main Program

Output










37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown




37





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown





38







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







39


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


40








(j) change

2007 15059745 000 15059745 000 15059745 000 15059745 000 2010 15703789 428 15648288 391 15528787 311 15494244 289 2015 17501995 1622 17438001 1579 16929857 1242 16890825 1216 2020 19431308 2903 18780540 2471 18355956 2189 17742894 1782 2025 21438103 4235 20650764 3713 19755145 3118 19023791 2632 2030 23693953 5733 22780286 5127 21271576 4125 20435507 3570 2035 26034285 7287 24945108 6564 22725696 5090 21747683 4441 2040 28697929 9056 27356813 8166 24523312 6284 23339737 5498 2045 31574234 10966 29893810 9850 26377074 7515 24903553 6537 2050 34673664 13024 32621827 11662 28351364 8826 26573564 7645









41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown






41







Truck 02970 00035 00027 0092

Rail 00252 00020 00006 0013




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown




40 60 80

100 120 140 160 180 200 220 240 260 280 300 22638 8464 31102 267 672 939 206 202 407 7012 4366 11379

25282 8167 33449 298 648 946 230 194 424 7832 4213 12044 231

28120 7848 35968 331 623 954 256 187 442 8711 4048 12759 258

31140 7508 38648 367 596 963 283 179 462 9646 3873 13519 287

34324 7150 41474 404 567 972 312 170 482 10632 3688 14321 318

37651 6776 44426 444 538 981 342 161 504 11663 3495 15158 351

41091 6389 47480 484 507 991 374 152 526 12729 3296 16024 385

44613 5993 50606 526 476 1001 406 143 548 13820 3091 16911 420

48181 5591 53773 568 444 1012 438 133 571 14925 2885 17809 456

51758 5189 56947 610 412 1022 471 124 594 16033 2677 18710 492

668

471 508 544 580 615 649 682 713 742 769

435 400 365 332 65412 3654

60094 63183 66182 69066 771 290

1032 1042 1052 1061 595 87

617 639 661 682 20262 1885 22147

635 62163 4019 733 319 565 96 19256 2073 21329 600 58784 4399 693 349 534 105 18209 2269 20479 565 55304 4791 652 380 503 114 17131 2471 19602 529


42





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown





43


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


44















45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown










45


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

46












47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








47


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


48


(Ouyang 2007)








49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


49






Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Within FAZ Delivery

50


1 1 1

1 1


I J ij ii j

I J iji j

D E d L

C E









N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown






N




51








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








speed = 30 mph

52





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown





f 1 k i1 j1


a A i1



53








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








2 hi3i1


54

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

2010 2030 2050 2010 2030 2050 2010 2030 2050 2010 2030 2050 1 122398 198095 289884 1346799 2179722 3189706 132177 213921 313042 418974 678087 992281 2 118928 178074 255794 1308617 1959416 2814603 128430 192300 276229 407096 609552 875591 3 117763 170262 242164 1295795 1873462 2664630 127171 183864 261510 403107 582813 828936 1 59553 87452 125428 655291 962273 1380130 64311 94439 135448 203853 299352 429343 2 57369 82258 116279 631257 905117 1279461 61952 88829 125568 196377 281571 398026 3 57101 81572 115284 628305 897564 1268517 61663 88088 124494 195458 279222 394621 1 204922 313583 448764 2254843 3450480 4937931 221293 338635 484615 701456 1073405 1536134 2 194166 273708 385010 2136485 3011717 4236423 209678 295574 415768 664636 936911 1317903 3 193313 268156 375555 2127098 2950629 4132381 208756 289579 405558 661716 917907 1285536 1 201300 338998 512772 2214988 3730134 5642239 217382 366081 553737 689058 1160402 1755236 2 194477 290456 427628 2139913 3196000 4705365 210014 313660 461791 665702 994239 1463785 3 193422 283980 418273 2128297 3124750 4602425 208874 306667 451688 662089 972074 1431762

Chicago

New York


Los Angeles

San Francisco


2010 2020 2030 2040 2050 1 341369 3 94 447954 1491 552488 1635 672867 1801 808486 1971

Los Angeles 1 2 331692 0 99 405994 415 496648 4 59 599286 511 713410 563 3 328442 389815 474861 570167 675396 1 166095 4 30 202874 660 243905 7 21 293513 798 349818 880

San Francisco 1 2 160003 0 47 191917 084 229417 0 84 274166 087 324302 086 3 159255 190315 227503 271809 321527 1 571529 601 718884 1531 874584 1694 1052235 1839 1251604 1949

Chicago 2 2 541529 0 44 634403 176 763372 2 07 909619 234 1073794 252 3 539150 623448 747888 888785 1047423 1 561427 407 751256 1700 945467 1937 1169480 2128 1430123 2259

New York 3 2 542398 055 655663 211 810081 2 28 986576 232 1192656 224 3 539454 642085 792022 964245 1166564


Case Study



usual 2 Polycentric


development


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


Impacts Models


Systems Models


Models






Freight Delivery




Mode Split

Trip Distribution




Forecasts



Trip Generation



Framework

56










57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown






57

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Introduction

58












59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







59






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown






d ij ij ij ij ij

i j i j Uij

1 if i g








60







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







61






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown






62

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

1

8

4 5 63

2

15 19

17

18

7

12 11 10 16

9

20

23 22

14

13 24 21

3

1 2

6

8

9

11

5

15

122313

21

16 19

17

20 18 54

55

50

48

29 51 49 52

58

24

27

32

33

36

7 35

4034

41

44

57

45

72

70

46 67

69 65

25

28 43

53

59 61

56 60

66 62

68 63

7673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

Origin

Destination

1

20


63



Origin

Destination

Origin Destination

Origin Destination







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







64



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



3333

3251

0008

0218-

0011

0071

8741145

160052

13260

10957

(a) Network




(b) Algorithm





(D = 0040)

254


2049 261 0009

0725-1997

4955 282

-4819

2102

-

65




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown




294 -051 295 050 829 -367 2142 641


125 -038 144 019 587 -597 2438 208


456 1005 636 -1184 864 4090 4082 -9393


481 -029 519 -008 421 -044 1104 -2176




Cost difference


Cost difference




66




67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



67



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown



68

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Fuel use projection

69



With congestion

GCAM (input-output)

( regression)



70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


70

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Conclusions





approaches

71








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown








72

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown

Future Research





systems

73







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown







74


75






76

12345678910

10 Commodity Types

77






Commodity unknown


75






76

12345678910

10 Commodity Types

77






Commodity unknown






76

12345678910

10 Commodity Types

77






Commodity unknown

12345678910

10 Commodity Types

77






Commodity unknown

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