Multimodal MultiScale Urban Traffic Control in Connected and Automated Cities [supplemental dataset]

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
• Subject/TRT Terms:
  • Autonomous vehicles
  • Connected vehicles
  • Cyclists
  • Human powered vehicles
  • Multimodal transportation
  • Pedestrians
  • Signalized intersections
  • Traffic signal control systems
  • Urban areas
  • Data files
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• 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. ; The total size of the ZIP file is 2.76MB.
• Content Notes:
  This dataset additional data and software in a GitHub repository. This repository is accessible online: https://github.com/Shakiba97/CET-593-MMSVCC
• Format:
  ZIP
• Funding:
  69A3552348326
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86516/dot_86516_DS1.zip
• File Type:
  [ZIP - 6.66 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:f9fced3c8bac29eab419580283641dde50b4f9cd1b3d6fe8028a1c0ab607dd3b49744e86f24ad0dcc258d6813a765ce15cd30239a9012add9bf3f619abedd37d