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<p>Emergency response time is a vital indicator of public safety effectiveness in dense urban settings like New York City, where even small delays can significantly affect survival rates. To support data-driven decision-making and enhance emergency response strategies, this project developed a Traffic Digital Twin (TDT): a high-fidelity, simulation- and AI-enhanced framework that replicates Emergency Medical Vehicle (EMV) operations under real-world, transient conditions. Developed in collaboration with the New York City Fire Department (FDNY), the TDT integrated diverse data sources and employed advanced calibration methods to accurately model EMV behavior and surrounding driver responses as detailed in a separate paper. This paper focuses on a complementary AI model, EMVAID, based on a Traffic Simulation-Informed Explainable Boosting Machine (TSI-EBM), designed to interpret key factors influencing EMV speeds. As an illustration of TDT decision support, it is then applied to optimize ambulance Cross Street Locations (CSLs), achieving up to 14.5% reductions in expected travel time during morning traffic while accounting for the likelihood of a congested CSL being unavailable. This study also demonstrates that proxy traffic features, such as 5-minute average speeds, can effectively support real-time EMV operations, underscoring the TDT’s potential as a scalable, interpretable decision-support tool for emergency response planning.</p>

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

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Farnoosh Namdarpour,
  • Fan Zuo,
  • Di Sha,
  • Hannah Bonestroo,
  • Jingqin Gao,
  • Joseph Y. J. Chow,
  • Kaan Ozbay

摘要

Emergency response time is a vital indicator of public safety effectiveness in dense urban settings like New York City, where even small delays can significantly affect survival rates. To support data-driven decision-making and enhance emergency response strategies, this project developed a Traffic Digital Twin (TDT): a high-fidelity, simulation- and AI-enhanced framework that replicates Emergency Medical Vehicle (EMV) operations under real-world, transient conditions. Developed in collaboration with the New York City Fire Department (FDNY), the TDT integrated diverse data sources and employed advanced calibration methods to accurately model EMV behavior and surrounding driver responses as detailed in a separate paper. This paper focuses on a complementary AI model, EMVAID, based on a Traffic Simulation-Informed Explainable Boosting Machine (TSI-EBM), designed to interpret key factors influencing EMV speeds. As an illustration of TDT decision support, it is then applied to optimize ambulance Cross Street Locations (CSLs), achieving up to 14.5% reductions in expected travel time during morning traffic while accounting for the likelihood of a congested CSL being unavailable. This study also demonstrates that proxy traffic features, such as 5-minute average speeds, can effectively support real-time EMV operations, underscoring the TDT’s potential as a scalable, interpretable decision-support tool for emergency response planning.