An AI-reinforced Traffic Digital Twin for Testing Emergency Vehicle Interventions

Emergency vehicle (EMV) response times have degraded due to increasing urbanization and resulting congestion. Evaluating interventions to mitigate this degradation is too costly to be done in the field. This project will build a traffic digital twin (TDT) to be developed in collaboration with FDNY as a virtual test bed to evaluate interventions and support decision-making and planning in a safe simulation environment. The TDT will be built on the open source Simulation of Urban Mobility (SUMO) microscopic continuous traffic simulation. Key challenges are incorporating AI to learn non-EMV driver responses to EMV signals (sirens, V2X technologies) and to train the TDT to different traffic states using historical traffic data and dispatch data from FDNY. The scope of work can be summarized as: (1) development and calibration of a baseline SUMO simulation for FDNY district M6 in Harlem, NYC; (2) combining traffic data and camera data at the same time to develop an AI model for traffic state prediction in the digital twin; (3) combining EMV global positioning system (GPS) data and the traffic state data to statistically learn non-EMV behavioral responses (response reaction time, etc.); and (4) developing simulation-based intervention optimization and test using out-of-sample observations

• Record URL:
  • https://c2smarter.engineering.nyu.edu/an-aireinforced-traffic-digital-twin-for-testing-emergency-vehicle-interventions/

Language

• English

Project

• Status: Active
• Funding: $146000
• Contract Numbers:

  69A3552348326

• Sponsor Organizations:

  New York City Fire Department

  New York City,   United States 

  Office of the Assistant Secretary for Research and Technology

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

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201
• Project Managers:

  Pohl, Lizzie

• Performing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201

  New York University Tandon School of Engineering

  6 Metrotech Center
  Brooklyn, NY  United States  11201
• Principal Investigators:

  Chow, Joseph

  Ozbay, Kaan

  Gao, Jingqin

• Start Date: 20231001
• Expected Completion Date: 20250331
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Emergency response time; Emergency vehicles; Traffic simulation
• Identifier Terms: SUMO (Traffic simulation model)
• Geographic Terms: New York (New York)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Security and Emergencies;

Filing Info

• Accession Number: 01897739
• Record Type: Research project
• Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
• Contract Numbers: 69A3552348326
• Files: UTC, RIP
• Created Date: Oct 28 2023 7:49PM