sustainable freight movement - mats utc (od) matrices od matrices capture the travel pattern of...

Sustainable Freight Movement

1

Carlos González-Calderón, Ph.D. Post-Doctoral Research Associate

Professor José Holguín-Veras’ Transportation Group

Volvo Research and Educational Foundations (VREF)'s

Center of Excellence for Sustainable Urban Freight Systems

Rensselaer Polytechnic Institute

MATS UTC Annual Meeting. August 6-7, 2015. Wilmington, DE

The Challenge - Global Drivers

Economic Globalization

Urbanization:

World’s population: 7+ billion people, 9 billion by 2045

In 2010, for the first time, 50% of world population is urban, by 2050, 70% of the world population will be urban

In US/Canada/Europe, the future is here: +80% urban

Impacts of the Internet on Supply Chains:

Millions of citizens expect fast and inexpensive deliveries

The diminished importance of proximity to customers as a competitive advantage, together with anti-freight attitudes and policies, leads to logistical sprawl

Increased Citizen Expectations

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3This is what we all want…

This is what we need to change…

Behavior change is the key

The Economy

Question: Who needs to change behavior??

Entire supply chains need to change behavior…

5

The shippers The receiversThe carriers

The Urban Freight System

The conglomerate of all the economic entities involved in the generation, transportation, consumption, and transformation of cargo

Key agents:

Producers, the ones that manufacture/produce the goods

Shippers, the ones that send the goods

Receivers, the ones that use the goods transported

Carriers, the ones that transport the goods

Ancillary functions: warehouses, distribution centers, etc.

The power relations:

Shippers have power over Carriers

Receivers have power over Shippers

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These are key to

behavior change

Inter-linkages among freight agents

Key insights

The carriers cannot unilaterally change operations, they are the weakest element of the chain

Although the carriers are the ones that produce the externalities, the actual source of the problem is the demand

In most cases, the carriers have no choice…

Due to competitive market forces:

Carriers are very efficient from the private point of view, not necessarily efficient from the social point of view

In many instances, if carriers could freely decide how to do things (without constraints), private optimal solutions would coincide with social optimal

The solution: modify the markets thru policy interventions

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What Could Be Done To Foster Sustainable Urban Freight Systems?

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Based on the research

conducted as part of

NCFRP 38

“Improving Freight

System Performance in

Metropolitan Areas”

For a comprehensive

Initiative Selector, see:http://transp.rpi.edu/~

NCFRP38PG/assessment.htm

http://transp.rpi.edu/~NCFRP38PG/assessment.htm

Key Insight

Achieving sustainability is all about behavior change

Technology-only approaches do not always lead to more sustainable outcomes:

If a technology leads to lower costs, it may induce demand (not necessarily the best outcome)

Demand management is needed to ensure a more sustainable outcome

If the technology does not lead to lower costs

Private sector is less motivated to embrace it

Other incentives are needed from public sector or citizens Implementation path is more difficult

Holistic approaches are the key

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Key Components of a Holistic Approach

Policies that foster behavior change

We (users, consumers, businesses, etc.) have to change the way in which we do things

Incentives are needed

Research helps understand how best to accomplish this

Technologies, operational changes, infrastructure:

Needed to reduce the consumption rates, mitigate/remediate the damage produced by economic activity, manage the use of resources, etc.

Redesign the economy and urban environments

Sustainability (or lack of) is a design problem

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Our research touches these three key components

How Could We Change Things?

By influencing the key decision maker so that they force a change in supply chains…

Remember the power relations:

Shippers have power over Carriers

Receivers have power over Shippers

Receivers Shippers Carriers

Implication: Convincing the receivers to participate in the quest for sustainability is ESSENTIAL

However, we (the Customers) have power over Receivers, Carriers, and Shippers. Let’s use it …

The real power-broker is the customer:

Customers Receivers Shippers Carriers

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Citizens-Led Change…

Citizens could provide the incentives needed to foster sustainability of supply chains:

A certification program that rates the degree of sustainability of the supply chains serving a establishment will

Provide information to citizens about what the companies are doing for sustainability

Lead citizens to patronize the businesses doing good

Ultimately, provide the incentives needed to foster transformation

A study by SRA found that diners are willing to pay more for dining, to foster sustainability

Big deal? Yes

Restaurants in NYC produce more truck trips than the port Retail customers may behave the same way…

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Freight Tour Synthesis and the Role of Traffic Count Sampling

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Carlos González-Calderón, Ph.D. Post-Doctoral Research Associate

José Holguín-Veras, Ph.D., P.E. William H. Hart Professor

Volvo Research and Educational Foundations (VREF)'s

Center of Excellence for Sustainable Urban Freight Systems

Rensselaer Polytechnic Institute

Trip Chain Behavior

Tour choice model: To estimate the node sequence comprising a tour

Tour flow model: To estimate the number of trips traveling along a particular node sequence

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Tour choice Tour flows

Characterization of Urban Freight Tours

Number of stops per tour depends on: Country, city, type of truck, the number of trip chains, type of carrier, service time, and commodity transported

NYC:

Average: 8.0 stops/tour

12.6% do 1 stop/tour; 54.9% do < 6 stops/tour, and 8.7% do > 20 stops

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Origin-Destination (OD) Matrices

OD matrices capture the travel pattern of trips

Freight data/information

Private firms usually do not provide information about cargo

OD matrices obtained from the field

Tend to be expensive

Labor intensive

OD Synthesis (ODS): Estimation of OD matrices from traffic counts.

ODS enable to capture travel patterns from secondary sources

More information available

Cheap and easy to collect (ITS)

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Freight Tour Synthesis (FTS)

Freight ODS produces an estimate of the freight trip flow OD matrix that matches secondary data, e.g., link traffic counts

Need to incorporate trip chain behavior of trucks in freight ODS: Freight Tour Synthesis (FTS)

Freight Tour Synthesis attempts to estimate entire delivery tours aggregately

Entropy maximization (EM) is adopted to develop tour-based urban freight travel demand models

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The equivalent model of formulation 2:Entropy-Based Freight Tour Synthesis18

M

m

mmm tttzMin1

)ln(

Subject to:

},...,2,1{,1

NiOta i

M

m

mim

( i )

T

M

m

mmT Ctc 1

( )

},...2,1{,1

QaVtp a

M

m

m

a

m

(γ)

},...,2,1{,0 Mmtm

Observed traffic

counts constraint

Trip production

constraints

Total impedance

constraint

Nonnegative

constraint

Minimization program to find the most likely ways to distribute tour flows if traffic counts are available

!

!...

1

)(2

2

1

m

M

m

t

tT

t

T

t

TCCWMax

Entropy function

Optimal Solution (First-Order Condition):

The number of tour flows following a tour is an exponential function of the Lagrange multipliers associated with:

Trip productions/attractions of nodes along that tour

Tour impedance

Observed traffic counts in links

Second-Order Condition

Objective function: Hessian is positive definite

Constraints: linear

Overall: convex program with one optimal solution

Entropy Maximization FOC and SOC19

N

i

Q

a

amamimim pcat

1 1

**** exp

24 Nodes, 76 Links

Nodes in the network represent the locations where deliveries and pick-ups are made.

Cost per mile and hour traveled of $1.74 and $52.10, respectively (Holguin-Verasand Brom, 2008)

Case Study: Sioux Falls Network20

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

2018 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

637673

30

7142

647539

74

37 38

26

4 14

22 47

10 31

The tour flows were calculated based on the negative exponential impedance function and the cost of the tours using an assumed parameter (β=-0.05)

The total number of tour flows is 9,855

The production and attraction of each node were estimated according to the tours stopping at the node

The traffic was obtained by:

Applying the EM model with the known impedance parameter

Assigning the tour flows to the shortest paths between centroids

This represents the “real” but unknown conditions in the ground

Tour Flows: Sioux Falls Network21

Assume that the decision maker could decide to “collect” any traffic counts he/she wants.

Inputs:

The constraints for productions-attractions (PAs),

Total cost, and

Different traffic counts (TCs): 0,5,10,15 and 20 links

Heuristics considered for FTS

1. Using “engineering judgment”

2. Selecting the links with the highest freight flow, and

3. Selecting the links with the highest traffic such that the links were not located in the same arterial

In all cases, the numbers of traffic counts were varied to see how the estimation of tours flows (T) and error change as more data were available

Traffic Count Sampling Heuristics for FTS22

Comparison of results for FTS23

Heuristic A

"Engineering

judgment"

Heuristic B

"Highest

flows"

Heuristic C

"Highest

flows in

different

arterial"

Heuristic A

"Engineering

judgment"

Heuristic B

"Highest

flows"

Heuristic C

"Highest

flows in

different

arterial"

1 Total cost and PAs 6980 6980 6980 215.3 215.3 215.3

2 Total cost, PAs and 5 TCs 7459 7609 7609 195.9 187.0 187.0




Case Constraints

Summation of all tour flows RMSE

y = 0.9965x

R² = 0.9933

0

100

200

300

400

500

600

0 100 200 300 400 500 600

Ob

serv

ed t

ou

r fl

ow

s

Estimated tour flows

1

10

100

0 5 10 15 20

RM

SE

Number of links with traffic counts

Heuristic A "Engineering judgment"

Heuristic B "Highest flows"

Heuristic C "Highest flows in different arterial"

(0% of total links) (7% of total links) (13% of total links) (20% of total links) (26% of total links )

Conclusions and Recommendations

This study develops a set of mathematical models for conducting FTS based on ME considering traffic counts

Few research projects on ODS considering truck characteristics, such as trip chain behavior

In order to minimize the estimation error associated with the FTS, the traffic counts should be collected such that the largest links with “independent” traffic are surveyed and included as inputs to the FTS

FTS makes it easier to estimate demand models

Improve the effectiveness of transportation planning

Estimate freight OD matrices at much reduced cost

Reduce pollution and warming gases

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Questions?

Carlos A. Gonzalez-Calderon, Ph.D.

Post-Doctoral Research Associate

Volvo Research and Educational Foundations' Center of Excellence

for Sustainable Urban Freight Systems

[email protected]

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