Design and Demonstration of an Arterial-friendly Local Ramp Metering Control System

Operating a joint network of highways and arterial streets in real time is challenging. The main challenges are twofold. Highways and arterials are highly inter-dependent, but may have their own operational strategies and systems that do not necessarily synchronize. As a result, traffic queues can spillover from highway to arterials, or the other way around, leading substantial congestion that worsens the system performance. Coordinating the signal control system on arterials and ramp metering control on ramps are key to mitigating such congestion. In addition, most signal or ramp metering systems deal with recurrent traffic congestion or normal traffic conditions. They can alleviate queues locally to some extent under non-recurrent congestion (being responsive or reactive), but are not designed to prevent queuing from the occurrence of incidents (being predictive) nor mitigate congestion for the joint network. To this end, managing traffic predictively (or proactively) and coordinating ramp metering and street signals among all relevant highway on-ramps/off-ramps can effectively improve the joint network performance. Transportation Systems Management and Operations (TSMO) refers to a set of strategies that could be utilized to mitigate system-level congestion, particularly non-recurrent traffic impacts, such as information provision, signalization, and access control. Though TSMO are technically available to practitioners, but what time and what strategy to engage remain unknown. Being predictive and proactive, and coordinating among all control strategies (e.g. street signals and ramp metering jointly) is the key to effective management of network-level traffic. Proactive operational management is highly dependent on accurate real-time traffic data and swift real-time traffic prediction. This research project addresses two problems for an integrated TSMO system: ahead-of-curve prediction and system-level signal and ramp metering coordination. We propose to develop theories, models and algorithms of machine learning to predict traffic patterns in real time being a typical recurrent pattern or non-recurrent pattern, and to optimize the timing plans for both ramp metering and street signals in the TSMO system. Prediction and operational strategies are intimately coupled. The prediction will be made by a machine that learns not only historical traffic patterns but also real-time data (possibly from multiple sources). Operational strategies are made and updated in real time to achieve management goals (e.g. minimization of total travel time) as a result of ahead-of-curve prediction of network impacts. In particular, the research will fuse multiple data sources related to highways and local street/intersections; develop an efficient network-level modeling framework enabled and validated by multi-source data; make real-time optimal signal plans and ramp metering plans; and finally quantify the network benefits of operational strategies to improve mobility/safety.


  • English


  • Status: Active
  • Funding: $50000
  • Contract Numbers:


  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

    University Transportation Center Program
  • Managing Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    Carnegie Mellon University

  • Principal Investigators:

    Qian, Sean

  • Start Date: 20210701
  • Expected Completion Date: 20220830
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01776446
  • Record Type: Research project
  • Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
  • Contract Numbers: 69A3551747111
  • Files: UTC, RIP
  • Created Date: Jul 13 2021 11:07AM