Measuring the Last-Mile: A Comprehensive Evaluation of Synthesis Approaches to Address Data Gaps for Local Freight Decision-Making (Phase 1)

Currently, few municipal or regional authorities have access to the disaggregate freight activity data needed for planning, operational decision-making, freight externality evaluation (e.g. air pollution, collision risk), or equity analysis. Due to stakeholder privacy concerns, freight data are often aggregated by geography and/or commodity, limiting direct applicability of published data for local analysis. As a result, local freight planning and analysis typically rely on one of three approaches to approximate local activity: (1) disaggregation of large national commodity flow datasets (e.g. Commodity Flow Survey and Freight Analysis Framework) using general estimates of economic activity; (2) modeling (e.g. freight trip generation, facility location, agent-based simulation, and route optimization models); or (3) direct estimation of activities using limited sensor and probe datasets, often obtained or purchased from private sector operators or commercial data providers. Each of these approaches suffers from severe limitations such as lack of timeliness, bias, lack of representativeness, reliance on unrealistic or unverifiable assumptions, and/or inability to validate results. Machine learning-based synthetic data generation methods may offer a potential approach to overcome limitations as well as operator privacy concerns to produce realistic data for local planning. This project represents the first phase of an expected multi-year effort to design and construct one or more synthetic last-mile freight datasets that can address existing data gaps to inform planning and operational decision-making by local transportation agencies.

• Record URL:
  https://tbd.ctr.utexas.edu/research-product/measuring-the-last-mile-a-comprehensive-evaluation-of-synthesis-approaches-to-address-data-gaps-for-local-freight-decision-making-phase-1/

Language

• English

Project

• Status: Active
• Funding: $479936
• Contract Numbers:

  69A3552344815

  69A3552348320

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Understanding Future of Travel Behavior and Demand

  University of Texas
  Austin, TX  United States 
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  City College of New York

  Civil Engineering, Steinman T-127
  140th Street and Convent Avenue
  New York, NY  United States  10031
• Principal Investigators:

  Conway, Alison

• Start Date: 20231201
• Expected Completion Date: 20250531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Data collection; Freight traffic; Machine learning; Regional planning
• Subject Areas: Data and Information Technology; Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01917640
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 6 2024 3:42PM