Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations

TL;DR

This paper introduces MLEVP, a machine learning-based strategy for emergency vehicle preemption that reduces delays for other vehicles while maintaining near-optimal ERV travel times, using real-time sensor data.

Contribution

The study develops a novel ML-driven EVP method that proactively manages downstream traffic queues, improving overall traffic operations during emergencies.

Findings

MLEVP achieves near-optimal ERV travel times.

It reduces delays for conflicting traffic.

The approach is validated through microscopic simulation.

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.