Research On Optimal Path Planning Method Based On Travel Plan Data

The acceleration of urbanization has led to the rapid growth of the urban population and the number of private cars,which causes traffic congestion and seriously affects travel efficiency and quality.The intelligent transportation system(ITS)has emerged to solve these problems,and one of its significant contents is vehicle path planning.Vehicle path planning refers to the timely recommendation of the optimal driving route for travelers according to the topology and the traffic condition of the road network,which can improve the travel efficiency of vehicles,alleviate traffic congestion and make full use of road resources.However,the path planning methods based on short-term traffic flow prediction mostly use historical traffic flow data and establish prediction models by mining the trend and regularity of traffic flow data.Thus,these methods ignore the chaotic characteristics of short-term traffic flow,so their prediction results need to be improved.The path planning method based on travel plan data(RPTP)is proposed to solve the problem in this paper.The RPTP method abandons the traditional way of obtaining future road conditions by traffic flow prediction and uses travel plan data to actively capture the future traffic demand of travelers on the road network.This method calculates the future road traffic conditions through the travel plan data and applies the calculation results to the vehicle dynamic path planning.Finally,the theories and methods based on travel plan data are formed.The main research contents and innovations of this paper are as follows:(1)The concepts and theories related to the new idea of “travel plan” are expounded.The concept of travel plan data is defined,and then the overall framework of path planning based on travel plan data is designed.Based on the conception of the travel plan,the difference and connection between traffic flow calculation and traffic flow prediction are compared to explain the advantages of travel plan data in reducing the uncertainty of short-term traffic flow and the advantages of applying the future road condition calculation results to dynamic path planning.(2)A model using travel plan data is established for calculating the future road network density.After dividing urban roads into sections,a road network system based on the travel plan is modeled to explain the calculation principle of future road conditions.On this basis,the road section density is selected as the index of traffic flow calculation,and then a model for calculating the future road network density using the travel plan route data is designed and described in the form of pseudo-code.(3)An optimal path planning method based on travel plan data is proposed in this paper.This method is based on predictive path planning and takes the calculation results of future road network density and road section length as the factors affecting path planning.Considering the impact of future road conditions on vehicles’ path planning,the spatial stacking method is applied in this paper to integrate the future multi-time road network density.Based on this,we improve the evaluation function of the D * Lite algorithm to take the impact of future road conditions,and the dynamic path planning method integrating future road condition information is finally realized.(4)Several simulation experiments are carried out using the SUMO simulation platform,which compares the simulation effects produced by the RPTP method with the static path planning method(SPP)and rolling path planning method(RPP)in the same condition.Experimental results shows that the RPTP method can reduce the travel time of vehicles,improve the traffic efficiency of the road network and alleviate the road traffic congestion.The results prove the superiority of the RPTP method compared with SPP and RPP.There are 41 figures,12 tables,and 97 references.