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

Details

• Creators:
  Liu, Zhichen ; Yin, Yafeng
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; University of Michigan Transportation Research Institute
• Corporate Contributors:
  Michigan Department of Transportation ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Case Studies Data Analysis End-to-end Learning And Optimization Learning Mathematical Prediction Networks Neural Networks Optimization Traffic Equilibrium Traffic Network Equilibrium Models Transportation Travel Demand Travel Demand Modeling Travel Time User Equilibrium

  More +
• Publication/ Report Number:
  CCAT Final Report 88
• DOI:
  https://doi.org/10.7302/27624
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (May 2024 – October 2025)
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  This project introduces an end-to-end framework for constructing integrated traffic network equilibrium models, which can serve as lightweight travel demand models, directly from multi-day aggregate traffic state observations. Unknown components on both the supply and demand sides are parameterized with computational graphs and embedded in a variational inequality to enforce user equilibrium conditions. The approach flexibly incorporates model-based, model-free (e.g., neural networks), or hybrid components and calibrates unknown parameters by minimizing discrepancies between observed and estimated traffic states. The framework was validated through numerical experiments with synthetic networks and empirical data from the Ann Arbor network. It demonstrated strong predictive accuracy for link flows under network changes and resilience to incomplete or noisy data. In the Ann Arbor case study, the framework reduced link travel time prediction error from 83.6% in the benchmark model to34.3% and successfully captured behavioral patterns such as reduced travel on weekends and snow days. The results also show that the framework has strong potential for prescribing optimal improvement plans to reduce congestion, as it integrates learning and optimization into a single data-to-decision pipeline. Overall, the proposed end-to-end framework enables automated construction and calibration of transportation network equilibrium models using cross-source data and supports the evaluation and prescription of strategies such as capacity expansion or congestion pricing. In practice, the framework can improve the efficiency of transportation planning, reduce both capital and operational costs, and guide more effective resource allocation to maximize public benefit.
• Format:
  PDF
• Funding:
  2022-0433 Z7 ; 221183NI
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:2ed7e8c55e42f162651baa578acd2eb087c8cc967e238b4e9e5dff9d12e1f6905a81a60fde7ee42ac845e7a47ffd2914249cc83f73149f7f76cb8fc8e239bcac
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88381/dot_88381_DS1.pdf
• File Type:
  [PDF - 11.07 MB ]