Improving Traffic Signal System Planning, Design, and Management with Big-Data-Enhanced ATSPM System

Details

• Creators:
  Li, Pengfei (Taylor) ; Mattingly, Stephen P. ; Wang, Peirong (Slade) ; Khadka, Swastik ; Shrestha, Sijan
• Corporate Creators:
  University of Texas at Arlington
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Traffic signal controllers
  • Traffic simulation
  • Traffic data
  • Literature reviews
  • Planning and design
  • Trajectory
  • Best practices
  • ATSPM
  • traffic big data
  • ATSPM digital twin
• Publication/ Report Number:
  FHWA/TX-26
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Texas Department of Transportation. Research and Technology Implementation Office
• Abstract:
  In this project, an ATSPM-in-the-loop simulation framework is developed to introduce the data-driven ATSPM framework into traffic signal planning and design. Literature review on ATSPM and Best practice survey are conducted first. Then the research team explores and documents how to set up two real-world open-source ATSPM systems: Utah-DOT ATSPM system and big-data-empowered UTA-In-Motion, an add-on module for ATSPM systems. Multiple integrating software tools are developed to parse simulation outputs into real-world ATSPM signal logs and WGS84 trajectories as the data feeds of ATSPM systems. Two case studies are conducted to demonstrate how this new framework can be used for traffic signal design projects. Last but not least, the research team presents their research outcome during state and national conferences and webinars as technology transfer activities.
• Format:
  PDF
• Funding:
  0-7160
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/90170/dot_90170_DS1.pdf
• File Type:
  [PDF - 13.48 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:9e48cafdd57e0802839f661fe8b95fb56dd93122c49730b80a45ee887c761ef1069e2e2af5b25307bbe47d155a6445d139d0c7832aa8d100f0770207c110d3de