Real-Time Data-Based Decision Support System for Arterial Traffic Management (Project J2)

Traffic congestion along arterial streets is increasingly becoming a critical issue that needs to be addressed by transportation agencies. Compared to the relatively mature management of freeways, arterial traffic operations and management are lagging behind. To address such a gap, Intelligent Transportation System (ITS) devices, such as traffic detectors, Bluetooth/Wi-Fi readers, and so on, are installed or planned to be installed along a number of arterial streets. The data generated from these devices provide an enriched source for monitoring arterial traffic and estimating performance measures. However, these measures are usually estimated offline to check arterial street performance and to verify the effectiveness of traffic operations. The real-time traffic operations of urban streets still rely more on visual examination of the videos generated from closed circuit television (CCTV) cameras at traffic management centers. To mitigate congestions during incidents or special events, some transportation agencies manually adjust signal timing based on operator's observations of queues. A robust and automated decision support system is needed to help transportation agencies to better manage arterial traffic in real time. The goal of this project is to develop a real-time data-based decision support system for arterial management. The developed system will not only automatically estimate system performance and identify the traffic state based on data from multiple sources, but also predict the short-term traffic conditions using advanced machine learning techniques. Recommendations will be further provided by the system based on predicted traffic condition as well as agencies’ past operational experience to assist agencies in determining optimal arterial traffic management and control strategies.

• Supplemental Notes:
  • A draft final report associated with this project is expected soon and will be peer reviewed. it is expected that the peer review process will be completed by the end of October 2022.

Language

• English

Project

• Status: Active
• Funding: $130506
• Contract Numbers:

  69A3551747104

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

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

  Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

  University of Florida
  365 Weil Hall
  Gainesville, FL  United States  32611
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  Florida International University

  10555 West Flagler Street
  Miami, FL  United States  33174

  University of Florida Transportation Institute

  P.O. Box 116580
  Gainesville, FL  United States  32611
• Principal Investigators:

  Hadi, Mohammed

• Start Date: 20181015
• Expected Completion Date: 20220630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Arterial highways; Closed circuit television; Decision support systems; Highway traffic control; Intelligent transportation systems; Machine learning; Performance measurement; Real time control; Route guidance; Strategic planning; Traffic congestion; Traffic signal control systems; Urban areas
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01682831
• Record Type: Research project
• Source Agency: Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
• Contract Numbers: 69A3551747104
• Files: UTC, RIP
• Created Date: Oct 9 2018 9:09AM