Multimodal MultiScale Urban Traffic Control in Connected and Automated Cities

Details

• Creators:
  Ban, Jeff ; Kitali, Angela E ; Naderian, Shakiba
• Corporate Creators:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC) ; New York University. Tandon School of Engineering
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology
• Subject/TRT Terms:
  Autonomous Vehicles Connected Vehicles Cyclists Human Powered Vehicles Multimodal Transportation Pedestrians Signalized Intersections Traffic Signal Control Systems Urban Areas
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Abstract:
  This project develops and tests the multiscale, multimodal signal-vehicle coupled control (M²SVCC) framework to jointly optimize traffic signal control and surrounding vehicles and other road users at urban intersections. Connected and automated vehicles and human-driven vehicles, with diverse energy types, are integrated, together with active road users (pedestrians and cyclists). The proposed M²SVCC model is tested through both simulation and real-world field test using the Mcity remote access testing facilities (Mcity 2.0). Testing results show that M²SVCC demonstrates superior performance compared to traditional signal control methods, significantly reducing delays, energy use, and conflicts across various scenarios. Future research will focus on integrating learning-based approaches and scaling the model to larger, more complex networks to further enhance multimodal urban transportation management.
• Format:
  PDF
• Funding:
  69A3552348326
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:40db5edd5a7d8732ff71b4f64b759cef30f2be4da048e8fbae295ce4426b8ca2e3eb60f2bbc38242854ad8eb2100f1f926b16acf8517dd48edc7d1e4fd98c259
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86515/dot_86515_DS1.pdf
• File Type:
  [PDF - 3.74 MB ]