End-to-End Learning Framework for Transportation Network Equilibrium Modeling

This project aims to outline a groundbreaking "end-to-end" transportation demand modeling framework, driven by deep learning techniques and empirical multi-source data. Unlike traditional models, which typically employ a multi-step process, this framework directly associates time-series observations of traffic patterns, urban land use, and socioeconomic features with prediction of future traffic flow distributions. The end-to-end modeling framework is designed to learn travelers travel and route choices while refining link performance functions that estimate travel time based on traffic flow. After calibration against empirical data, the proposed framework can recommend optimal policies or projects for enhancement, thereby facilitating informed decision-making. By utilizing passively collected trajectory data, this framework aims to significantly improve modeling accuracy and the realism of behavioral representation, without additional costs for data collection in the existing modeling system.

• Record URL:
  https://ccat.umtri.umich.edu/research/u-m/end-to-end-learning-framework-for-transportation-network-equilibrium-modeling/

Language

• English

Project

• Status: Active
• Funding: $156062
• Contract Numbers:

  69A3552348305

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Michigan Department of Transportation

  Van Wagoner Building
  425 W. Ottawa Street
  Lansing, MI  United States  48909
• Managing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Project Managers:

  Stearns, Amy

  Bezzina, Debra

• Performing Organizations:

  University of Michigan, Ann Arbor

  Department of Civil and Environmental Engineering
  2350 Hayward
  Ann Arbor, MI  United States  48109-2125
• Principal Investigators:

  Yin, Yafeng

• Start Date: 20240528
• Expected Completion Date: 20251027
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Equilibrium (Systems); Machine learning; Network analysis (Planning); Traffic forecasting; Travel demand; Vehicle trajectories
• Subject Areas: Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01929637
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3552348305
• Files: UTC, RIP
• Created Date: Sep 5 2024 10:35AM