Smart Emergency Vehicle Routing And Preemption In Connected Environments

The rapid urbanization process has led to significant challenges in efficiently responding to emergencies in increasingly congested urban traffic.Improving the response time of emergency vehicles,handling emergencies more effectively,and ensuring public safety are crucial concerns.Intelligent network connection systems,a cutting-edge technology in intelligent transportation,have shown promising potential in emergency rescue and traffic management scenarios.This study aims to integrate intelligent network connection technology into the emergency vehicle rescue process.Firstly,this research develops a mixed-integer linear programming(MILP)model for optimizing the timing scheme and collaborative control of emergency vehicle intersections.The model incorporates different communication ranges to enable emergency vehicles to clear entrance lanes preemptively.Two timing conversion strategies are proposed to transition an intersection from normal signal control to an emergency-optimized intersection.Furthermore,different priority access control models are applied to four-way intersections,ensuring the smooth passage of emergency vehicles under various signal light conditions.The Gurobi+Python environment is used to solve the MILP model,demonstrating the effectiveness of the optimization model and signal timing strategy in prioritizing emergency vehicles at intersections.Secondly,this study establishes an emergency vehicle speed regulation and signal light coordination control model based on shock wave theory for cases where emergency vehicles can only communicate over short distances.The model estimates the maximum queue length and predicts queue dissipation time at intersections.By coordinating the speed of emergency vehicles and intersection timing schemes according to the signal light state and queue dissipation time,the model ensures that emergency vehicles can pass through intersections without stopping,reducing delays and enabling efficient passage through congested intersections.Selecting an actual intersection as the experimental object,a joint simulation was conducted based on the SUMO simulation software and the Python platform.The results confirm the effectiveness of the model in enabling emergency vehicles to pass quickly through congested intersections.Finally,this study integrates intersection-level control with road network-level planning.Intersection control algorithms for various communication ranges are used to establish path planning models within the intelligent network environment.The ELSN model is employed to fit peak-hour travel times for each road segment,and a travel time estimation model is developed.The Fenton-Wilkinson method approximates the path travel time distribution,leading to the creation of a path travel time estimation model.An improved A~* algorithm is proposed to solve the emergency vehicle route planning model,considering both travel time reliability and optimization.In long-distance communication scenarios,the heuristic function for inlet lane preemption is the optimal path cost with the minimum expected travel time.For short-distance communication,a reliability factor is introduced into the predictive function,and a path planning model is established,optimizing reliability and travel time for the subpath from the node to the destination under historical conditions.To demonstrate its feasibility,a case simulation of the road network was conducted using SUMO simulation software,and historical data for each section was outputted.The emergency vehicle route planning model is demonstrated through simulations on the Python platform using actual road network data.The two path planning models for different communication ranges proposed in this study both enable rapid emergency vehicle response.In a long-distance communication environment,the priority of nodes is stronger,reducing the travel time of emergency vehicles by 6% compared to nodes.