Traffic Predictive Control

The advent of ubiquitous traffic sensing provides unprecedented real-time, high-resolution data of traffic conditions that elucidate historical trends and current traffic conditions, yet traditional signal control approaches are designed to operate with limited or no real-time and/or historical data. Adaptive control schemes adjust to accommodate current traffic demands yet have been observed to react slowly to changing conditions. In contrast, non-adaptive signal timing schemes are designed based on limited and often outdated historical measurements. This project seeks to fully leverage historical and real-time traffic data and proposes traffic predictive control for improved efficiency on arterial corridors. The proposed approach learns statistical trends from collected historical data using principle component-based decomposition techniques. Then, real-time measurements are compared against historical trends to predict traffic flow minutes or hours into the future. Based on this prediction, preemptive control strategies accommodate deviations in traffic conditions before they occur. Additionally, these historical trends are used to identify when anomalous traffic conditions have occurred or are likely to occur soon. This anomaly detection serves as a component of decision support systems to notify when traffic conditions are likely to require additional resources.


  • English


  • Status: Active
  • Contract Numbers:

    2016 - TO 045 - 65A0529

  • Sponsor Organizations:

    California Department of Transportation

    1227 O Street
    Sacramento, CA  United States  95843

    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 California Center on Economic Competitiveness in Transportation (UCCONNECT)

    University of California, Berkeley
    Berkeley, CA  United States  94720-1782
  • Performing Organizations:

    University of California, Los Angeles

    Los Angeles, CA  United States 
  • Principal Investigators:

    Coogan, Samuel

  • Start Date: 20160501
  • Expected Completion Date: 20170430
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01628707
  • Record Type: Research project
  • Source Agency: University of California Center on Economic Competitiveness in Transportation (UCCONNECT)
  • Contract Numbers: 2016 - TO 045 - 65A0529
  • Files: UTC, RiP, STATEDOT
  • Created Date: Mar 8 2017 12:49PM