Research On The Path Planning Method Of Urban Traffic Network From Different Perspectives

In the new era,with the rapid development of urbanization,more and more people are concentrated in the city,which brings a huge traffic demand to the urban traffic network.The traffic congestion caused by this causes a huge problem to the urban development.How to alleviate the traffic congestion and improve the traffic efficiency has become an increasingly realistic problem in the current urban development process.Path planning method is an important part of intelligent transportation system(ITS),it can effectively alleviate traffic congestion and improve traffic efficiency,thus it has been widely studied by scholars.In order to realize the reasonable assignment of traffic in urban traffic network,it is necessary to plan the best route scheme for all vehicles.The definition of the best route scheme varies from perspective.From the perspective of users,the best route scheme should be able to reduce the traffic delay and vehicle travel time as much as possible;from the perspective of system,the best route scheme should be able to reduce the traffic network congestion and optimize the traffic network status as much as possible.Many path planning methods use mathematical models to solve path planning problems in urban traffic road networks,but it is well known that it is not difficult to establish a good mathematical model,the difficulty is to establish a less complex model for effective optimization.In addition,many path planning methods rely on the road impedance model which is too mechanical and short-sighted.It only considers the influence of the current road state on the road impedance,and does not consider the state of neighbor roads and the possible subsequent state of the road network.Therefore,it has poor adaptability in the dynamic urban traffic assignment.In this paper,a user-oriented path planning method Sb TA-TSA is proposed,and a new dynamic programming model is proposed and constructed.Combined with the existing optimization model,the TSA algorithm is proposed to make the shortest path solution process of time-dependent adaptive traffic demand.The Sb TA-TSA method obtains the dynamic information of the road network by simulating the traffic road network,and continuously iteratively calculates from the user perspective to make the planned path gradually adapt to the road network state to obtain higher traffic efficiency.A system-oriented path planning method MRLA is proposed.Agents are deployed on all road segments and vehicles in the network respectively.The road segment agent uses reinforcement learning to construct the road network into an intelligent impedance system.It can adapt the road network traffic state from the system perspective to adjust the road segment impedance to optimize the road network state.The vehicle agent plans a differentiated path for itself according to the historical driving trajectory,thereby further reducing the probability of traffic congestion.This paper proves the effectiveness of the proposed method based on relevant theories,and conducts a large number of experiments based on the real road network.It is proved that the Sb TA-TSA method proposed in this paper obtains higher calculation performance than Sb TA-RSA and Sb TA-FSA methods through adaptive traffic demand;the parameter settings that maximize the performance of the MRLA method are discussed;It is proved that both methods adaptively adjust its path planning strategies from different perspectives to obtain a good ability to improve traffic efficiency and alleviate traffic congestion.