Smarter Multi-modal Traffic Signal Control with Both Floating Sensor Network and Fixed Sensor Network

The objective of this project is to develop a comprehensive framework with a set of models to improve multi-modal traffic signal control, by incorporating advanced floating sensor data (e.g. GPS data, etc.) and traditional fixed sensor data (e.g. loop detectors, etc.). Especially, the project is interested in addressing the challenges of multi-modal signal control under non-recurrent conditions, such as traffic incidents and planned special events, since non-recurrent congestions usually account for more than 50% of the total congestions. In order to accomplish this goal, an 18-month project is defined in this proposal with a multidisciplinary team assembled with two principal investigators (PIs) from transportation engineering and computer science, respectively. First, the project will conduct a comprehensive interview with transportation professionals, who can bring up existing state-of-practice, open issues and future challenges in multi-modal traffic signal control. The results of this interview will be compared with the recent complete interview with police officers, who have real-world traffic control experiences under non-recurrent events (N. Ding, He, and Wu 2014). Second, a two-component traffic data analysis will be performed on a variety of multi-modal data sources, including passenger cars, transit buses, light rail and emergency vehicles as well as commercial trucks, bicycles, and pedestrians. One component is to fuse multi-source and multi-modal data sets and predict the traffic state in near future. The other one is to identify the anomaly condition in traffic network, caused by traffic incidents (e.g., collisions, disabled cars, hazard materials, etc.) or special events (e.g., football game, parade, marathon, etc.). Third, multi-modal signal control algorithms will be developed to leverage the results derived from traffic data analysis, under both recurrent and non-recurrent congestion conditions. Finally, the proposed framework will be evaluated by microscopic simulation VISSIM and externally developed signal control modules. With consideration of advanced multi-modal and multi-source data, this research closely aligns with University Transportation Research Center's (UTRC's) Focus Area #4: System modernization through implementation of advanced and information technologies as described in the Request for Proposal (RFP). Through alleviating traffic congestion and improving safety of the highway system, this work will also contribute to UTRC consortium's themes in Economic Competitiveness and Livable Communities. The project team will work closely with Niagara International Transportation Technology Coalition (NITTEC), the City of Buffalo, and New York City on how the proposed algorithms and models could help in the development of a multi-modal traffic management and operations Decision Support System (DSS). The results will be disseminated to transportation authorities through webinars or workshops for workforce training.


  • English


  • Status: Completed
  • Funding: $166510.00
  • Contract Numbers:


  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    University Transportation Research Center

    City College of New York
    Marshak Hall, Suite 910, 160 Convent Avenue
    New York, NY  United States  10031
  • Project Managers:

    Eickemeyer, Penny

  • Performing Organizations:

    State University of New York, Buffalo

    212 Ketter Hall
    Buffalo, NY  United States  14260
  • Principal Investigators:

    He, Qing

  • Start Date: 20140301
  • Expected Completion Date: 0
  • Actual Completion Date: 20151130
  • Source Data: RiP Project 36167

Subject/Index Terms

Filing Info

  • Accession Number: 01557846
  • Record Type: Research project
  • Source Agency: University Transportation Research Center
  • Contract Numbers: 49997-32-25
  • Files: UTC, RIP
  • Created Date: Mar 26 2015 1:01AM