Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations
For traffic engineers and emergency services, this work addresses the trade-off between emergency vehicle speed and general traffic delay, offering a data-driven approach to improve overall network efficiency.
This study develops a machine learning-based emergency vehicle preemption strategy (MLEVP) that proactively clears downstream traffic queues to reduce emergency vehicle response time while minimizing delay to other vehicles. In a simulation case study, MLEVP achieved near-optimal emergency vehicle travel times with reduced impact on conflicting traffic.
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.