Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach

TL;DR

This work tackles the problem of timely EMS response in NYC by introducing an intersection-aware accessibility framework that integrates road-network structure, intersection density, and population data under a $\tau=4$ minute benchmark. The methodology combines a graph-based travel-time model with an intersection-delay factor $\alpha$ and a Voronoi-population assignment to identify vulnerable regions. Calibrations against NYC 911 data show that the baseline $\alpha=15\,\text{s}\cdot\text{m}^2$ underestimates real-world delays, while applying EMVLight can halve intersection delays and expand access, achieving $\sim70\%$ hospital coverage and $95\%$ of residents within $\tau=4$ minutes. The results support targeted urban-planning and policy interventions but acknowledge limitations such as the absence of real-time traffic data and static EMS-location assumptions, pointing to future validation and scalability work.

Abstract

Emergency response times are critical in densely populated urban environments like New York City (NYC), where traffic congestion significantly impedes emergency vehicle (EMV) mobility. This study introduces an intersection-aware emergency medical service (EMS) accessibility model to evaluate and improve EMV travel times across NYC. Integrating intersection density metrics, road network characteristics, and demographic data, the model identifies vulnerable regions with inadequate EMS coverage. The analysis reveals that densely interconnected areas, such as parts of Staten Island, Queens, and Manhattan, experience significant accessibility deficits due to intersection delays and sparse medical infrastructure. To address these challenges, this study explores the adoption of EMVLight, a multi-agent reinforcement learning framework, which demonstrates the potential to reduce intersection delays by 50\%, increasing EMS accessibility to 95\% of NYC residents within the critical benchmark of 4 minutes. Results indicate that advanced traffic signal control (TSC) systems can alleviate congestion-induced delays while improving equity in emergency response. The findings provide actionable insights for urban planning and policy interventions to enhance EMS accessibility and ensure timely care for underserved populations.

Figures (17)

• Figure 1: EMS Stations (Blue) and Hospitals (Red) in NYC.
• Figure 2: All NYC streets following NYC Street Centerline.
• Figure 3: Signalized intersections layout in Brooklyn and Queens
• Figure 4: Census tracts in Staten Island. Each polygon represents a census tract.
• Figure 5: Population distribution of NYC census tracts.