Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations
2026-05-13 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed a new method called MLEVP to help emergency vehicles, like fire trucks, get through traffic lights faster. Unlike regular systems that only focus on speeding up the emergency vehicle, their method also tries to reduce delays for other cars nearby. They used machine learning with real-time data from sensors and traffic simulations to decide the best times to change traffic lights. Their tests showed that MLEVP can keep emergency vehicles moving quickly while causing less wait time for other drivers.
Emergency Vehicle Preemption (EVP)Machine LearningTraffic Signal ControlMicroscopic Traffic SimulationPTV VissimReal-time Sensor DataRegression ProblemTraffic Queue ManagementSignalized Intersections
Authors
Somdut Roy, Michael Hunter, Abhilasha Saroj, Angshuman Guin
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.