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Development of the Pennsylvania Statewide Commodity-Based Freight Model. 11th TRB National Transportation Planning Applications Conference May 7, 2007. Wade White, AICP, Citilabs Brian Wall, Pennsylvania Dept. of Transportation Patrick Anater, Gannett Fleming.

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development of the pennsylvania statewide commodity based freight model

Development of the Pennsylvania Statewide Commodity-Based Freight Model

11th TRB National Transportation Planning Applications Conference

May 7, 2007

Wade White, AICP, Citilabs

Brian Wall, Pennsylvania Dept. of Transportation

Patrick Anater, Gannett Fleming

penndot and analytical tools a historic perspective
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 process importance and satisfaction with travel options

B-

High Grade/

Low Importance

High Grade/

High Importance

6

10

General Public, n=751

8

3

C+

5

1

7

4

11

9

C

Low Grade/

Low Importance

Low Grade/

High Importance

2

Somewhat IMPORTANCE Very

Important (Scale of 1 - 4) Important

Public Input to the Planning ProcessImportance and Satisfaction with Travel Options

Travel Options (1-11)

  • Roads for cars
  • Roads for trucks
  • Public transit (buses & rail)
  • Bicycling facilities
  • Pedestrian facilities
  • Airline services
  • Deep water port facilities
  • Bus service between cities
  • Passenger train service between cities
  • Rail freight service
  • Ride sharing (car and van pool)
overview of the model development
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
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
Geography
  • WithinPennsylvania-
    • 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

Logistics Nodes
commodity groups
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

STC Codes, Model Groups and Descriptions
basic structure
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
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
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
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
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
Generation

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

  • 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.

TLN have no production and consumption

Study Area

Internal Area

External Area

External Zone

TLN

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

distribution
Distribution

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

  • 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

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

External fractions set by user plus trends

Study Area

Internal Area

External Area

External Zone

TLN

mode choice
Mode Choice

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

  • For Long-Haul Only
  • Multinomial logit models stratified by commodity and distance class
  • Choice models use by mode and commodity class
    • Time
    • Cost
    • Constant

Only for long-haul flows

Study Area

Internal Area

External Area

External Zone

TLN

logistics node model
Logistics Node Model

Partitions into Long-Haul Direct Flows by mode

  • 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 TLN Flows and Short-Haul TLN Flows by mode

Study Area

Internal Area

External Area

External Zone

TLN

fine distribution model
Fine Distribution Model

Allocate Destinations with Weights based on socioeconomic data

Distribute from fine origin to fine destination using gravity models

  • 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

10

25

30

25

10

25

35

25

15

Coarse Zone

Fine Zone

Allocate Origins with Weights based on socioeconomic data

review bringing the flows to the vehicle model
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
slide22

Generation

Coarse zone level

P & A by CC

Distribution

Long-Haul Flows by CC

Direct Short-Haul Flows by

CC by Truck

Mode Choice

Long Haul Flows by

Mode & CC

TLN

Direct Long-Haul Flows by

Mode & CC

Long-Haul Flows to TLN by

Mode & CC

Short-Haul Flows to TLN by

Truck & CC

Fine Distribution

Fine zone level

Direct Short-Haul Flows by

CC by Truck

Direct Long-Haul Flows by

Mode & CC

Long-Haul Flows to TLN by

Mode & CC

Short-Haul Flows to by

truck & CC

vehicle models
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
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

Study Area

Internal Area

External Area

External Zone

TLN

standard vehicle model
Standard Vehicle Model

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

  • 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’

A ‘Big Zone’

Study Area

Internal Area

External Area

External Zone

TLN

service trips
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
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
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
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
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
changes in organizational thinking
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
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
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