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.

翻译：紧急响应车辆（ERV），如消防车，用于挽救生命和减少财产损失。通常实施紧急车辆优先（EVP）策略，通过在其路径上接近信号交叉口时给予绿灯信号，为ERV提供通行权。EVP操作通常以最小化ERV延误为目标进行优化。本研究旨在在保持ERV行程时间接近最优的同时，减少网络中其他车辆的延误。开发了一种基于机器学习的EVP策略，称为MLEVP，利用实时传感器数据（包括车辆检测、信号指示和ERV位置）确定多个下游交叉口的EVP触发时间。MLEVP主动清除下游交通队列，以缩短ERV响应时间，同时限制对冲突交通流的影响。在案例研究中，基于PTV Vissim中校准的信号走廊测试平台的微观仿真开发了MLEVP。将EVP问题表述为回归问题，并使用在仿真生成数据上训练的机器学习模型进行求解。结果表明，所提算法能够在最小化对冲突交通影响的同时产生近乎最优的ERV行程时间。