Integrating Microsimulation in a Multi-scale Local Freight Activity Model

This project builds on results from prior TBD Center projects to advance the development of a multi-scale, synthetic data-based simulation framework for evaluating urban freight policy and infrastructure interventions. While prior phases enabled representation of total activity within a traffic analysis zone (TAZ), they do not yet represent location- or segment-specific activity within the zone. However, many urban freight interventions of interest to local cities — such as street design changes, lane or signal prioritization, parking and loading regulations, and segment-specific access controls — require this more detailed granularity. This Phase 3 study aims to address this gap. The aim of this project is to define a simulation framework that could integrate TAZ-scale vehicle activity inputs, be implemented within a TAZ at a population scale to simulate segment-level activities, and be adaptable to simulate multiple local decision types. This project phase has three objectives: (1) to identify best-available microsimulation approaches for evaluating micro-scale freight interventions; (2) to assess the utility of machine learning approaches — in particular reinforcement learning — to improve upon static decision-assignment processes within these simulations; and (3) to develop a scalable and adaptable framework for microsimulation of freight interventions that integrates TAZ-scale truck paths with high-resolution segment-level activity. Key project tasks include: a comprehensive synthesis of existing freight microsimulation studies, feasibility analysis of reinforcement learning for freight operator route choice and behavioral modeling, development of a proposed graph structure for simulating varied freight-related interventions within a traffic microsimulation model, and evaluation of common simulation tools (e.g., VISSIM, SUMO) to assess their native capability to represent the proposed graph structure.

Language

• English

Project

• Status: Active
• Funding: $200,000.00
• 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:

  University of Texas at Austin

  Austin, TX  United States  78712
• 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: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Freight traffic; Machine learning; Microsimulation; Route choice; Traffic analysis zones; Transportation policy; Truck traffic; Urban goods movement
• Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting; Policy;

Filing Info

• Accession Number: 02001768
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: Sep 4 2026 3:04PM