Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization

This project explores using machine learning techniques to spatially and temporally customize predictive functions in queuing based macro simulations of traffic. Its objective is to replace much slower "one-size fits all" micro simulators so that reliable adaptive traffic control and optimization will be possible, which is a very practical end-goal. The first goal is to create machine learning algorithms for learning how to predict the travel time of a car on a specific segment which may include difficult segments which represent signaling such as intersections or toll booths. Data will ultimately come from car telemetry monitoring. The predictor functions will be further used in this project to make much more efficient, reliable, and location sensitive traffic simulator which is necessary for future optimization algorithms. The initial experiments will attempt to reproduce VISSIM output but much more efficiently. The second goal is then to create traffic optimization algorithms using the research team's simulator to estimate cost of a signaling strategy. The team will be using several algorithms they have used in the past for evacuation planning as a starting point.

• Record URL:
  http://depts.washington.edu/pactrans/research/projects/using-machine-learning-to-customize-traffic-prediction-for-high-performance-traffic-analysis-and-optimization/

Language

• English

Project

• Status: Completed
• Funding: $80000
• Contract Numbers:

  69A355174110

• Sponsor Organizations:

  Pacific Northwest Transportation Consortium

  University of Washington
  More Hall Room 112
  Seattle, WA  United States  98195-2700

  Office of the Assistant Secretary for Research and Technology

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

  National Institute for Advanced Transportation Technology

  University of Idaho, Moscow
  115 Engineering Physics Building
  Moscow, ID  United States  83844-0901
• Project Managers:

  Heckendorn, Robert

• Performing Organizations:

  National Institute for Advanced Transportation Technology

  University of Idaho, Moscow
  115 Engineering Physics Building
  Moscow, ID  United States  83844-0901
• Principal Investigators:

  Heckendorn, Robert

• Start Date: 20210516
• Expected Completion Date: 20220515
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Machine learning; Mesoscopic traffic flow; Traffic signal timing; Traffic simulation; Travel time
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01784881
• Record Type: Research project
• Source Agency: Pacific Northwest Transportation Consortium
• Contract Numbers: 69A355174110
• Files: UTC, RIP
• Created Date: Oct 16 2021 9:44PM