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

TL;DR

This paper develops an intersection-aware EMS accessibility model for NYC, identifying vulnerable regions and demonstrating that reinforcement learning-based traffic control can significantly improve emergency response times and coverage.

Contribution

It introduces a novel intersection-aware model for EMS accessibility assessment and proposes a reinforcement learning framework to optimize traffic signals, reducing delays and improving coverage.

Findings

Densely interconnected areas face significant EMS accessibility deficits.

Reinforcement learning reduces intersection delays by 50%.

EMS coverage increases to 95% within 4 minutes for NYC residents.

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.