Coordinated Control Method For City Emergency Vehicle Priority Passage In The Internet Of Vehicles Environment

The capability of emergency vehicles(E-vehicles)to reach the destination safely and timely is a crucial factor in dealing with an emergency rescue successfully.However,with the population aggregation and the development of vehicles and roadway traffic in cities,the probability of traffic congestion and traffic accidents trended to normalization,which has a negative impact on the traffic efficiency of E-vehicles.In recent years,Internet of Vehicles(IOV)technology which is based on the Internet of Things has been thriving in the meantime.The real-time information acquisition,information sharing and real-time control provides strong support for improving the travel efficiency,reliability,and safety of E-vehicles.Therefore,this dissertation,based on the IOV environment,analyzed the distinction of traffic operational characteristics between the IOV environment and traditional environment.Then,the priority degrees were classified by combining the basic features of E-vehicles with its management and control stipulations in the emergency rescues.Lastly,the intelligent decision-making control process of E-vehicles was designed.On this basis,the priority traffic control strategies and methods of emergency vehicles at road sections,intersections and road networks were systematically studied,the research content is as follows:(1)Control methods of E-vehicles’ priority for road sections in the IOV environment were proposed.First,the lane-changing process was studied in the IOV environment.A multi-vehicle-cooperative lane-changing strategies were established based on simulation data by taking E-vehicles’ minimum passing time as the objective.Then,the platoon interval modification model and platoon convergence model were built based on IOV technology,which contributes to an IOV-Based Automatic Way-Giving Method(IAWM)at the platoon level to realize E-vehicles priority at the road sections.The was conducted on SUMO road section model via Tra CI(Traffic Control Interface)by using Python.The simulation scenario of IAWM was built which took dual-way four-lane as an example to simulate and verify IAWM.A comparative experiment was designed to analyze the sensitivity of traffic flow densities of the IAWM.The results show that IAWM can greatly improve the operational efficiency of E-vehicles.IAWM make the time needed for adjusting trajectories of the platoon increase with the increase of density as well.(2)Control methods for E-vehicles’ priority at intersections in the IOV environment were studied.The travel process of E-vehicles at the intersections were analyzed through the signal status of E-vehicles’ arrival time and the Spatio-Temporal Map.According to various signal statuses,signal priority strategies were presented including forwarding green,extended green and inserted green.The different signal control optimization algorithms,green light compensation schemes and priority control flows of these three signal priority strategies were studied.Then,the speed guidance control model of E-vehicles in the IOV was constructed.Based on these studies,the coordinate control method of signal priority and speed guidance in the IOV environment was proposed.Finally,the simulation program of the coordinate control method of signal priority and speed guidance was implanted to SUMO intersection model by Python,A simulation program for the priority of emergency vehicles at intersections in IOV environment was compiled,which took actual intersections as samples to simulate and verify the coordinate control method.The results show that the coordinate control method ensured the delay of E-vehicles passing time drop drastically.Nonetheless,the efficiency of E-vehicles will decrease to some extent with the increase of traffic volume.(3)The dynamic route planning methods of E-vehicles’ priority in the IOV environment were designed.Spatio-Temporal Map was adopted to analyze the travel characteristics of E-vehicles and its influence on O-vehicles on the road sections and intersections.Accordingly,two parameters,travel time of E-vehicles and the delaying of O-vehicles,were modeled respectively.Taking travel time of E-vehicles and the total delay of O-vehicles as optimal objectives with proper regard to the priority level of E-vehicles,the dynamic route planning for E-vehicles’ priority was constructed.The process of dynamic route planning for E-vehicles’ priority was proposed.This process realized road resistance dynamic updating by selected the Conv-LSTM to modeling the prediction of network traffic flow,and achieve the accurate and rapid E-vehicles’ route planning by Genetic Algorithms.Finally,the model and algorithms were verified by constructing SUMO simulation road networks and collecting relevant road data.The results show that the calculation errors of travel time model of E-vehicles in different periods is sufficiently small,and the dynamic route planning methods of E-vehicles’ priority can effectively improve the traffic efficiency of E-vehicles.Therefore,it can be used for E-vehicles dynamic route planning,and achieve the system optimization under different priority levels.The outcomes of this dissertation can provide decision support for E-vehicles routing in the IOV environment and contribute to the research in the field of E-vehicles priority and Vehicle-Road Cooperation under the IOV environment.