Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations

2026-05-13

AI Summary

This study addresses the challenge of balancing emergency vehicle preemption with regular traffic efficiency by proposing a multi-intersection coordinated preemption strategy (MLEVP). The approach formulates emergency vehicle priority as a regression task, leveraging real-time data—including vehicle detections, signal states, and emergency vehicle positions—to predict and optimize preemption timing across multiple downstream intersections. Training data are generated using PTV Vissim microscopic simulation, and a machine learning model is employed to proactively clear queues and coordinate conflicting traffic streams. Experimental results demonstrate that MLEVP achieves near-optimal travel times for emergency vehicles while significantly reducing average delay for conflicting traffic movements.

Abstract

Emergency response vehicles (ERVs), such as fire trucks, operate to save lives and mitigate property damage. Emergency vehicle preemption (EVP) is typically implemented to provide the right-of-way to ERVs by giving green signals as they approach signalized intersections along their routes. EVP operations are usually optimized to minimize ERV delay. This study seeks to reduce delay experienced by other vehicles in the network while keeping ERV travel time near its optimum. A machine learning-based EVP strategy, termed MLEVP, is developed to determine EVP trigger times at multiple downstream intersections using real-time sensor data, including vehicle detections, signal indications, and ERV location. MLEVP proactively clears downstream traffic queues to reduce ERV response time while limiting delay on conflicting traffic movements. In the case study, MLEVP is developed using a calibrated microscopic simulation of a signalized corridor testbed in PTV Vissim. The EVP problem is formulated as a regression problem and solved using machine learning models trained on data generated from the simulation. Results demonstrate that the proposed algorithm can produce near-optimal ERV travel times while minimizing impacts on conflicting traffic.

Problem

• Emergency Vehicle Preemption
• Traffic Delay
• Machine Learning
• Signalized Intersections
• Traffic Operations

Innovation

• machine learning
• emergency vehicle preemption
• traffic signal optimization
• real-time traffic control
• microscopic simulation