California Statewide Freight Forecasting Model: Progress Report

Transcription

1 California Statewide Freight Forecasting Model: Progress Report Stephen G. Ritchie, Professor of Civil Engineering and Director, Institute of Transportation Studies University of California, Irvine Presented at Freight Project Working Group Meeting June 19,

2 Project Team from UC Irvine Principal Investigator: Professor Stephen Ritchie University of California, Irvine Professor Michael McNally, University of California, Irvine Assistant Professor Joseph Chow Ryerson University, Toronto (formerly ITS-Irvine Postdoc) Andre Tok, Ph.D. Postdoctoral Scholar University of California, Irvine Soyoung Iris You, Ph.D. Assistant Project Scientist University of California, Irvine Graduate Students: Miyuan Zhao Fatemeh Ranaiefar Pedro Camargo Daniel Rodriguez-Roman Kyungsoo Jeong Paulos Lakew Kate Hyun Neda Masoud 2

3 Outline Overview California Statewide Freight Forecasting Model (CSFFM) Proposed FAZ Scheme Primary Modules Simplified View Summary of Progress Budget Milestones for Major Tasks Completed Tasks Progress Update on Q4 for Key Additional Tasks Network Quality Control Integration with CSTDM Upcoming Tasks Upcoming Tasks for Q1 CSTDMS Integration & Peer Advisory Committee Meeting 3

4 OVERVIEW 4

5 California Statewide Freight Forecasting Model Commodity-based model Forecasts the long haul flow of commodities and commercial vehicles on freight infrastructure as a function of socioeconomic conditions and infrastructure parameters Primary Modules Mode Vehicle Class Commodity Module : Predicts commodity flows Transshipment Module : Assigns commodities to specific modes Network Module : Assigns truck and rail modes to routes Truck Rail Water Air (+Truck-Air) Multiple Modes FHWA Class : Class 5 to 13 5

6 Continued.. Policy-sensitive model within California addressing: Socioeconomic conditions Land use policies Supply chains Multimodal infrastructure investments < Note > The current stage of CSFFM is converging in concept to a structural direct demand model (B.1) and a transshipment network model (B.2) from the Enhancement Modules for domestic commodity flows. => Capable of addressing domestic land use policies and supply chains 6

7 Proposed FAZ Scheme 7

8 Primary Modules Socioeconomic Variables Exogenous Variables : Import/Export FAF OD Road/Rail Network Characteristics Air/Water Impedances Vehicle Inventory and Use Survey (VIUS) Weight-In-Motion (WIM) Characteristics by Rail Classes Commodity Module Structural Direct Demand Output: Domestic Commodity FAZ OD Import/Export Output: Import/Export Commodity FAZ OD by Linked Mode IMPLAN FAF Port Data (POLB & POLA) Waterborne Transborder PMA (Pacific Maritime Association) Transshipment Module (FAZ / Gateway + TLN) Output: Commodity (Transload Facilities) OD By Mode Truck OD By Truck Class Rail OD By Rail Class Rail Waybill Waterborne T-100 Network Module (Stochastic Assignment) Output: Truck Network Flows By Commodity By Truck Class Time of Day Season 8 : input variables for the model : input data for calibration

9 Simplified View IMPLAN, FAF3 VIUS, WIM Import/Export FAF ODs Factor to FAZ ODs Split out Gateway-FAZ Socioeconomic Variables by Zone Structural Direct Demand Model: FAZ ODs Transshipment Model : FAZ/Gateway + TLN Factor Truck ODs to 9 Truck Classes Road/Rail Network Characteristics Air/Water Impedances FAF3 Rail Waybill, Waterborne, T-100 Stochastic Assignment: Truck + Rail Network Flows : input variables for the model : input data for calibration 9

10 Some key advantages of our CSFFM approach over other recent state models Yields elasticities of OD flow to socioeconomic variables (instead of just commodity generation) Identifies explicit correlations between OD pairs of different commodity groups (land use patterns, some supply chain effects) Includes congestion effect at transshipment nodes Explicitly treats empty haul ODs as a function of transshipment capacities 10

11 SUMMARY OF PROGRESS 11

12 Budget Total funding: $1,400,000 Total expenditures thru Q4 (projected): $368,974 Expenditures in Q4 (projected): $127,953 % budget expended: 26% % of work complete: ~30% On schedule: yes Within budget: yes 12

13 Milestones for Major Tasks Develop/Calibrate Vehicle Network Model Develop /Calibrate Transshipment Model Develop /Calibrate Commodity Model Seasonal Factor Enhancement Inventory Temporal Assignment Enhancement Truck Tour Enhancement Transshipment Enhancement Commodity Enhancement Data and System Prep Scenario Analysis/Report Update Model 2010 Validate Model Oct 2011 Jan 2012 Jun 2012 Jan 2013 Jun

14 Completed Tasks thru Q Data Preparation (Task A.1) Defined Freight Analysis Zones (FAZs) and commodity groups Obtained UCD repository Prepared Socioeconomic data at FAZ level for base year Disaggregated FAF data at FAZ level for base year Obtained STB Rail waybill data Identified transshipment/transloading nodes and collected the corresponding data Determined mode-specific OD matrices for air, water, rail, and truck for base year Compiled WIM data Prepared truck data from WIM and local truck studies Obtained factors commodity-to-vehicle, annual-to-day, time-ofday 14

15 Completed Tasks thru Q , cont. Model Construction (Task A.2) Coded up fractional split model Defined base road/rail network Recommended software platform Identified truck restrictions and tolls Purchase /Prepared software platform and hardware Coded commodity demand model (B.1) Developed script to implement B.2 Model Calibration (Task A.3) Coordinated with Statewide Rail Plan Network Development Began editing/reconciling road and rail network for base year 15

16 Completed Tasks thru Q , cont. Enhancement Module B.1 (Demand) Completed literature review Obtained IMPLAN data and extract data for testing model Formulated and coded IO-constrained direct demand model estimator Tested Model using initial network skims Enhancement Module B.2 (Transshipment) Completed literature review Developed transshipment network model Tested Model with simple network Developed calibration algorithm for California network Enhancement Module B.3 (Urban Truck Tours/Time of Day/Zone Disaggregation) Completed literature review Formulated truck tour model Prepared truck data for evaluating model Tested model 16

17 Completed Tasks thru Q , cont. Enhancement Module B.4 (Inventory Temporal Assignment) Completed literature review Completed model formulation Developed test scenario Tested model Enhancement Module B.5 (Policy Sensitive Seasonality Factors) Started literature review 17

18 Progress in Q Data Preparation (Task A.1) Collected additional agriculture, energy, and other socioeconomic data Compiled WIM 2010 data Obtained truck count data on arterial network Compared vehicle classification using WIM and truck count data Continued to build transshipment flow/od matrices Model Construction (Task A.2) Finalized model design based on Task B.1 and B.4 Began coding commodity model Began coding transshipment model Continued to refine road and rail networks 18

19 Continued Model Calibration (Task A.3) Continued to edit/reconcile road network for base year Continued to edit/reconcile rail network for base year Built a framework for network quality control Enhancement Model B.1 (Demand) Continued evaluation of commodity enhancement model Enhancement Model B.2 (Transshipment) Continued to test transshipment calibration algorithm Enhancement Model B.4 (Inventory Temporal Assignment) Continued to evaluate feasibility of model for annual-to-day assignment Developed the parameter calibration algorithm Began the data preparation for initial California implementation on two FAF3 zones for year

20 KEY ADDITIONAL TASKS 20

21 Network Quality Control Assurance of network quality is critical to transportation modeling procedures. We propose the following scheme: Road Rail Rail yards Network Seaports, Airports, Other Gateways* Paths/Connectivity Procedure Compare CSFFM s network with other regional model networks; 1) Link, 2) Load Stock (lane mile), and 3) Capacity Compare all rail yards with freight movement (2009 Rail Waybill); Freight only rail link and mixed traffic (Transit + Freight) to be considered. 1) Volumes loaded and unloaded per commodity group 2) Yard s location within the county 3) Satellite image such as Google Earth of the yard Connections to the road and rail network Paths should be presented; 1) All paths, 2) Zoom for FAZ of Origin, and 3) Zoom for FAZ of Destination * Border with Mexico or neighboring states 21

22 Integration with CSTDM Changes from CSTDM Some TAZ were divided to observe Air Basin boundaries (22 TAZs). Rail Network was replaced Level of Detail and Purpose Representation of short-haul (service vehicle and Intrazonal (FAZ) trip) flows vs CSTDM needs to be addressed Joint Assignment Two different procedures are considered; 1) Disaggregate to TAZ and perform a multi-modal-multi-class assignment in CSTDM, or 2) preload flows in freeways/highways only < Upcoming Task see later > Technical meeting to discuss integration of CSTDM and CSFFM planned for July

23 Upcoming Tasks for Q Data and Model System Preparation Build scheme for calibration using WIM 2010 data Cluster WIM stations by FAZs Evaluate ATRI sample data (if available) Complete refinement of road and rail networks for CUBE Commodity Model Implement/calibrate commodity model Develop commodity model in CUBE Transshipment Model Complete transshipment flow and OD matrices Develop commodity model in CUBE Evaluate feasibility of B.4 model for annual-to-day assignment 23

24 Continued < Proposed Framework for Converting Commodity-to-Truck Class Flow > Complete framework for converting commodity-to-truck flow Investigate regeneration of VIUS factors for use in CSFFM 24

25 Continued Network Model Develop Integration module between CSFFM and CSTDM Define performance measures for validation Build a framework for validation Literature review on seasonal effects CSFFM/CSTDM Integration and Peer Advisory Committee Meeting 25

26 PEER ADVISORY COMMITTEE 26

27 Name Title Affiliation Scott Drumm Manager, Research and Port of Portland Market Information Anne Goodchild Assistant Professor University of Washington J. Douglas Hunt Professor HBA Specto, & University of Calgary Ram Pendyala Professor Arizona State University William (Bill) Rogers Senior Program Officer TRB Rolf Schmitt Team Leader, Freight Analysis FHWA Eric Shen Director of Transportation Port of Long Beach Planning Frank Southworth Principal Research Scientist Georgia Institute of Technology 27

28 Combined CSFFM/CSTDM Technical Meeting and Peer Advisory Committee Meeting Tentatively scheduled (by Caltrans) at ITS-Irvine in July 2012 Topics: Review of both overall and detailed approaches and progress for CSFFM Technical issues related to integration of CSFFM and CSTDM Development of integration plan Findings to be documented at Anticipated attendees: Caltrans, Office of Travel Forecasting and Analysis, Division of Transportation System Information Doug Maclvor, Chief, Transportation/Freight Modeling and Data Branch Chad Baker, Kalin Pacheco, Diane Jacobs University of California, Irvine (see UCI team slide) Technical Peer Advisory Committee (see previous slide) Cambridge Systematics: Ron West (PI, CSTDM Update) 28

29 QUESTIONS OR COMMENTS? 29