Dynamic Trajectory Control and Signal Coordination for a Signalized Arterial with Significant Freight Traffic

Freight traffic affects the performance of a road network in a more sensitive and significant way compared to other traffic with respect to mobility, environment, and safety. This is due to the complexity of the characteristics of the mixed-class traffic. For example, heavy trucks need extra distance and time for deceleration and acceleration, and their interactions with conventional vehicles can cause more uncertainties to the traffic flow due to their different lengths, speeds, and acceleration performances. As a result, a traffic bottleneck may appear on road segment where freight traffic is significant even though the overall volume is not high enough to cause congestion if the traffic composition is not truck heavy. What is more, for a signalized arterial, the coordination often fails when the traffic is composed of a large portion of trucks. This has been shown in the research of Freight Mobility Research Institute (FMRI) first-year project. In year II this proposed project tries to look into the area of freight signal priority control, which is related to control and information technology. Given the existing infrastructure, the improvement of freight traffic operation can be conducted at the tactical and operational level. In the research of FMRI first-year project, the connected vehicle techniques are assumed so that signal information and estimated queuing information are treated as known inputs for the optimization of individual truck speed profile. Besides, in FMRI first-year project, the optimization is conducted for individual trucks given signal information and estimated queuing information as inputs. This second-year FMRI research focuses on the vehicle dynamics of trucks. In this proposed research, multiple trucks dynamic trajectories and their interactions with the conventional cars will be investigated, and an analytical tool of traffic flow performance will be developed. Based on the analytical models, control strategies are developed to schedule the trajectories of trucks/cars dynamically to improve the mobility of a corridor, assisted by the new coordination strategies of signals. Two levels of the strategies are defined in the scope of this research: At the first level, the strategy takes into account the vehicle dynamics and optimizes the trajectories of trucks/cars given signal timing plans, signal states and traffic conditions. Real-time decisions and behaviors of vehicle motions and their interactions with other vehicles and with the infrastructure are analyzed. At the second level, the strategies will model the interactions among trucks and conventional vehicles while a new coordination strategy of signals is established, in which the timing variables are used as decision variables. While developing these strategies, the factors of robustness, optimality, predictability will be considered if necessary, and realistic factors such as truck market penetration rate, truck characteristics, speed variability, and signal types will be considered.


    • Status: Active
    • Funding: $70000
    • Contract Numbers:


    • Sponsor Organizations:

      United States Department of Transportation - FHWA - LTAP

      1200 New Jersey Avenue, SE
      Washington, DC    20590
    • Managing Organizations:

      Freight Mobility Research Institute

      Florida Atlantic University
      Boca Raton, FL  United States  33431
    • Performing Organizations:

      Texas A&M Transportation Institute, College Station

      Texas A&M University System
      3135 TAMU
      College Station, TX  United States  77843-3135
    • Principal Investigators:

      Zhang, Yunlong

    • Start Date: 20180901
    • Expected Completion Date: 0
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01682651
    • Record Type: Research project
    • Source Agency: Freight Mobility Research Institute
    • Contract Numbers: 69A3551747120
    • Files: UTC, RiP
    • Created Date: Oct 3 2018 11:47AM