development of the pennsylvania statewide commodity-based freight model

Pennsylvania Statewide Freight ModelPennsylvania Statewide Freight Model

Development of the Pennsylvania Statewide Commodity-Based

Freight Model

Wade White, AICP, CitilabsBrian Wall, Pennsylvania Dept. of Transportation

Patrick Anater, Gannett Fleming

11th TRB National Transportation Planning Applications Conference

May 7, 2007


PennDOT and Analytical Tools—A Historic Perspective

PennDOT’s Credibility & Funding Crises—1970s PennDOT & State Transportation Advisory Committee

– New Directions for PennDOT– Program and Budgetary Control– Strategic Planning & Management

This “Model” was successful for a generation Resource Constraint Renews True Planning Focus PA Mobility Plan as the catalyst for improving

analytical capabilities


Public Input to the Planning ProcessPublic Input to the Planning ProcessImportance and Satisfaction with Travel OptionsImportance and Satisfaction with Travel Options

2.8 3.8

General Public, n=751

C+

C

1

3

4

5

6

9

Low Grade/High Importance

10

Somewhat IMPORTANCE Very Important (Scale of 1 - 4) Important

B-

High Grade/High Importance

Low Grade/Low Importance

High Grade/Low Importance

8

7

11

2

1. Roads for cars2. Roads for trucks3. Public transit (buses & rail)4. Bicycling facilities5. Pedestrian facilities6. Airline services7. Deep water port facilities8. Bus service between cities9. Passenger train service

between cities10. Rail freight service11. Ride sharing (car and van

pool)

Travel Options (1-11)


Overview of the Model Development

Identification of data needs Analysis of the freight and GIS databases (Global

Insight, CFS, Freight Analysis Framework) Identification of commodity groupings and

development of zone systems, base year matrices and networks

Development of model structure within Cube Cargo Linkage of data and model adjustment


Basic Characteristics

3 line-haul modes:– Road– Rail– Inland Waterway– Intermodal is estimated transport logistic

chain models

Ten (10) commodity classes Estimates annual commodity flow

matrices by mode and commodity class

Estimates annual and daily truck matrices by large and small truck class


Geography

Within Pennsylvania- – Consistent with Statewide Travel

Demand Model– 1001 Zones

Border States Defined same as Travel Model

Other States- Individual Zones

4 external zones; 3 for Canada and 1 for Mexico


Logistics Nodes

Ports– Port of Wilmington,

DE– Port of Baltimore,

MD– Port of NY/NJ– Port of Philadelphia,

PA– Port of Pittsburgh,

PA– Port of Erie, PA– Port of Cleveland,

OH

Rail Yards– NS Rutherford Yard- Harrisburg, PA– NS Trafford PA Intermodal Yard– Newark/Elizabeth NJ Intermodal Yards– NS Bethlehem PA Yards– NS Taylor, PA NS Yard– Conrail, CSX, NS Philadelphia, PA Rail

Yards– NS Harrisburg Yard

Distribution Centers– Clearfield, PA Walmart Distribution

Center– Bedford, PA Walmart Distribution

Center


Commodity Groups

1 Unprocessed Agricultural/Fishing Products 2 Unprocessed Ores & Petroleum 3 Coal 4 Processed Food & Tobacco 5 Textiles & Apparel 6 Lumber & Wood Products 7 Chemical, Petroleum or Coal Products 8 Clay, Glass, Concrete, Stone & Leather 9 Machinery & Metal Products 10 Miscellaneous


STC Codes, Model Groups and Descriptions

1 1 Farm Products 8 1 Forest Products 9 1 Fresh Fish Or Marine

Products 10 2 Metallic Ores 11 3 Coal 13 2 Crude Petroleum Or

Natural Gas 14 2 Nonmetallic Minerals 19 2 Ordnance Or Accessories 20 4 Food Or Kindred Products 21 4 Tobacco Products 22 5 Textile Mill Products 23 5 Apparel Or Related

Products 24 6 Lumber Or Wood Products 25 6 Furniture Or Fixtures 26 6 Pulp, Paper Or Allied

Products 27 6 Printed Matter 28 7 Chemicals Or Allied

Products

29 7 Petroleum Or Coal Products 30 8 Rubber Or Misc Plastics 31 8 Leather Or Leather Products 32 8 Clay, Concrete, Glass or

Stone 33 9 Primary Metal Products 34 9 Fabricated Metal Products 35 9 Machinery 36 9 Electrical Equipment 37 9 Transportation Equipment 38 9 Instrum, Photo Equip,

Optical Eq 39 9 Misc Manufacturing Products 40 10 Waste Or Scrap Materials 41 10 Misc Freight Shipments 43 10 Mail Or Contract Traffic 46 10 Misc Mixed Shipments 49 10 Hazardous Materials Or

Substances


Basic Structure 3 groups:

– Network costs: used to estimate zone to zone matrices of door-to-door travel time and cost by each of the line-haul modes for each commodity class. Line-haul modes are Truck, Rail and waterway.

– Cube Cargo: estimation of commodity and truck flows

– Trip Table Preparation: Prepare Truck Trip Tables for PATDM Assignment


Network Cost

Road: applies Cube Voyager programs to the roadway network to estimate paths

Rail: applies Cube Voyager to the rail network to estimate paths

Water: network of major waterway services and rivers


Truck, Rail, and Water Cost Estimation

Estimation of travel times and costs via:– Network based travel time

with user assumptions on:• Pickup and drop-off time• Driver rules: Federal Hours of

Service (truck)• Average speed

– Network based distance used with cost parameters:

• Cost per ton-mile by commodity type

– Based on user assumptions and published cost data

– Network developed from FHWA roadway and rail network


Network Costs

The network cost group provides:– Zone to zone door-to-door cost per ton by mode and commodity

group– Zone to zone door-to-door travel time by mode and commodity

group– Zone to zone travel distance by mode

Data are important elements in the estimation of the distribution, mode choice and transport-logistics nodes models


Model Process Generation: estimates annual tons

of commodities produced and consumed by zone by commodity class

Distribution: distributes goods by commodity class

Mode Choice: estimate modal shares of long-haul flows

Logistics Nodes Model: partitions the long-haul matrices by mode and commodity class into direct flows and transport chain flows

Fine Distribution Model: for each of the matrices redistributes from coarse zones to the fine zones

Vehicle Model: converts the estimated annual commodity flows by truck into number of heavy trucks and light trucks

Service Model: estimates daily urban service truck trips


Generation Regression models on

socioeconomic attributes (zonal data) and constants by commodity class and country

Use of special generators to represent external generated commodities: ports by location of facility and commodity class

Trend rates to represent production efficiencies and other factors not represented in the regression models by commodity class and country

User specified values for the amount of production exported to external zones and the amount imported to the internal zones by commodity class

Trend rates to represent trends in the level of import and export. Internal Area External Area External Zone TLNStudy Area

External zones controlled with ‘shift’/ “singpoint” variables to fix imports and exports by commodity class and trend rates

TLN have no production and consumption

Production and consumption by commodity class and country based on socioeconomic attributes of the zones and trend rates (efficiencies)


Distribution User assumption on percentage of

goods that are to be considered short-haul and long-haul by commodity class

Trend rates to represent changes in short-haul and long-haul percentages by commodity class

Short-haul trips are considered to be transported by truck and are distributed using gravity models by commodity class Impedance is cost

Segments the remaining long-haul flows into those remaining ‘internal’ and those remaining ‘external’ by commodity class

Adjusts internal and external fractions by user assumption on trends by commodity class

Distributes internal, import and export long-haul flows using gravity models by commodity class

Impedance is a generalized cost using a linear combination of time, distance and cost by mode weighted by the mode choice coefficients

Internal Area External Area External Zone TLNStudy Area

Set assumption (%) and trend rates on what is short- and long-haul flow by commodity class

Short-haul flows will be truck only and distributed with gravity models

External fractions set by user plus trends


Mode Choice

For Long-Haul Only Multinomial logit models

stratified by commodity and distance class

Choice models use by mode and commodity class– Time– Cost– Constant


Estimate percentage truck, rail and waterway by commodity class based on door-to-door shipment time and shipment cost and constant

Only for long-haul flows


Logistics Node Model

Partitions the long-haul matrices by mode and commodity class into direct and TLN flows

Definition of zone location of TLNs and the zones that they serve

Definition of directionality of TLN flows and selection of TLN

Product is matrices by commodity group segmented into:

– Long-haul direct flows by mode

– Long-haul to/from TLN flows by mode

– Short-haul to/from TLN flows by truck


Define location of TLN

Define service area of TLN

Partitions into Long-Haul Direct Flows by mode

Partitions into Long-Haul TLN Flows and Short-Haul TLN Flows by mode


Fine Distribution Model Distributes models from

coarse zone system to fine zone system

The fine zones are smaller and nested under a coarse zone. These flows are distributed to the fine zones encompassed by the coarse zone using:

– a weight to establish a small sub-matrix of the fine zone matrix based on parameters and fine zone level zonal data

– and a gravity model using, distance as the impedance, to infill the individual cell values

– It is possible to override these models to represent particular points in the system

Fine ZoneCoarse Zone

30

10

15

25

25

25

25

35

10

Allocate Origins with Weights based on socioeconomic data

Allocate Destinations with Weights based on socioeconomic data

Distribute from fine origin to fine destination using gravity models


Review: bringing the flows to the vehicle model Generation gives P & C by zone and commodity class

Distribution distributes two sets of matrices:– short-haul flows by commodity class which are assumed to be truck flows;

and– long-haul flows by commodity class which go to mode choice

Mode Choice splits the long-haul flows into long-haul flow matrices by mode and commodity class

The long-haul modal matrices from Mode Choice are segmented into flows that:– travel directly from zone of production to zone of consumption (Direct long

haul flows by commodity class) and,– flows that will use a TLN. The flows that go via TLNs are segmented into:

• short haul segment by mode and commodity class• long-haul segment by mode and commodity class


Generation

P & A by CC

Distribution

Direct Short-Haul Flows by CC by Truck

Long-Haul Flows by CC

Mode Choice

Long Haul Flows by Mode & CC

TLN

Direct Long-Haul Flows by Mode & CC

Short-Haul Flows to TLN by Truck & CC

Long-Haul Flows to TLN by Mode & CC

Fine Distribution

Direct Short-Haul Flows by CC by Truck

Direct Long-Haul Flows by Mode & CC

Short-Haul Flows to by truck & CC

Long-Haul Flows to TLN by Mode & CC

Fine zone level

Coarse zone level


Vehicle Models The vehicle models estimate matrices of light and heavy truck

trips by using vehicle models and load factors.

Two vehicle models:– Touring Vehicle Model

• Used for flows where the origin is a TLN• And, where the user selects specific zones

– Standard Vehicle Model• Used in all other cases

Results are combined to provide a truck trip matrix for assignment– Heavy long-haul trucks

– Heavy short-haul trucks

– Light short-haul trucks

By default, these matrices are annual truck flows (resulting from the annual commodity flows). Matrix manipulation can be used to estimate daily and hourly flows by season if so desired.


Touring Vehicle Model

Vehicles are assumed to have the same start and end zone but make intermediate stops to load and unload.

Heavy computations so limit use to TLN and selected zones.

Estimates number of vehicles based at the origin using the flows from that zone and average load factors.


Generated tour from a TLN and back doing pickups and drop-offs


Standard Vehicle Model

Model assumes that all vehicles make trips of the form A-B-A.

Return load is a function of the commodity flow in the opposite direction.

Can modify using ‘big zones’ enlarging the area considered for a return load.


By default, the standard model creates direct round trips between the two zones. The probability of a return load depends on the flow of goods in the ‘back direction’

With the use of ‘Big Zones’ can include neighboring zones when calculating probability of a return load. This generates a simple tour.

A ‘Big Zone’


Service Trips All modeling to this point concerns the movement of

goods.

The service model is used to estimate urban service truck trips such as:– Repair men (e.g. elevator repair)– Small shopkeeper taking goods from a wholesaler to a local restaurant, etc.

Used directly on the fine zone system

Estimates generation using regression models based on zone type and socioeconomic data

Trips are distributed using gravity models.


Truck Trip Table Creation

Matrix manipulation to estimate average daily truck trips for assignment

Single Unit (Light Trucks)

Combination Unit (Heavy Trucks)

Through (E-E) Truck Trips


Running the model – base year

A user menu allows the modification of various attributes for the scenario:– Input highway network– Truck pick-up and drop-off delay

time– Driver parameters (break and

sleep)– Costs per ton-mile by mode– Rail pick-up and drop-off delay– Waterway pick-up and drop-off

delay– Average rail travel speed


Potential Use for the PA Statewide Freight Model

To Answer Policy Questions

– Effects of alternative growth scenarios on freight movement• What if regional development patterns change?• What if major freight facilities are developed?

– Effects of alternative policies on freight movement• What if tolls were increased?• What if the price of fuel continues to increase?

– Impacts of major projects on freight movement• What if a two-lane US highway was widened to four lanes?• What if major access improvements to a region were advanced?


Outputs for PennDOT’s Use

Region to Region Commodity flows– 10 Regions

Tonnage and Value by Mode Key Freight Corridors Future expected growth System Impacts

0%

50%

100%

150%

200%

250%

% C

han

ge 2

002 to

2030

Unprocessed Agricultural/ Fishing Products

Unprocessed Ores & Petroleum

CoalProcessed Food & Tobacco

Textiles & Apparel

Lumber & W

ood Products

Chemical, Petroleum

or Coal Products

Clay, Glass, Concrete, Stone & Leather

Machinery & Metal Products

Miscellaneous

Commodity


Changes in Organizational Thinking

Current LRTP serves as a change agent

Major awareness raising challenges

Creating a culture valuing analytically based planning

TDM in an emerging Multi-Modal Agency

Leveraging strong PennDOT—MPO and RPO Partnership

Paradigm shifts for evaluation—Core System, State of the System, Performance Based


Changes in PennDOT Capabilities

Leadership Support

Analytical Skill Building from the ground up

Applied Training—Learning through implementation

Capacity Building Bridging Agency & Consultant

Marketing capabilities to PennDOT staff and leadership

Continuing to Expand Horizons—Applying and expanding the tools as warranted


Implications

TDM and freight modeling can’t be in a vacuum

Strategic Integration of T-O-P Thinking– Technical, Organizational, and People

Building bridges among a wide range of organizations (internal & external)

Communicating Progress & Raising Awareness

Fostering Experimentation, Innovation, and Risk Taking


Questions?

development of the pennsylvania statewide commodity-based freight model

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