Research On Traffic Demand Forecasting Model Of Autonomous Vehicles

With the rapid development of computer algorithms,sensor accuracy and control theory and other technologies,the development of self-driving cars is faster and faster,and is expected to be put into use on the road in two decades.Combined with perfect decision-making behavior programming,self-driving cars can avoid the randomness of human operation and respond more accurately and quickly,thus greatly improving driving safety.Travelers using self-driving cars can rest or work while traveling to improve the comfort level.Since self-driving cars have a shorter reaction time,they can keep a shorter distance from cars and also help reduce road congestion.Therefore,it is necessary to study the traffic network demand forecasting model with self-driving cars.Firstly,this paper introduces the current situation of the development of self-driving cars,analyzes the opportunities and challenges of its current development,and then expounds the inevitability of self-driving cars joining the urban traffic system.The classical four-stage allocation theory and common traffic allocation methods are introduced.This paper analyzes the assumptions and methods that the four-stage model should be followed in the addition of self-driving cars.By modifying the utility function to represent the characteristics of self-driving cars,the four-stage distribution model containing self-driving cars is gradually integrated,so as to obtain a four-stage distribution model that can be applied to the traffic distribution in future cities.However,from the summary of literature on modeling,adoption rate and integration model of self-driving cars at the present stage,it is found that the research on this aspect is still very weak,and the research in this paper is of practical significance.A classical four-stage model with autonomous vehicle is established.Based on the four-stage traffic model,the autonomous vehicle is integrated into the model by making basic assumptions that meet the calculation conditions of the model.Through calculation,the part of generation and attraction generation is modified.The gravity model is used to modify the traffic distribution.Add the utility function representing the self-driving car to conduct a new traffic mode division;The BPR function is improved by combining the proportion of autonomous vehicles,and the route with the minimum generalized cost is selected for traffic allocation.Finally,a four-stage model of the combination of autonomous vehicles and traditional vehicles is obtained.The effects of different proportion of autonomous vehicles on traffic system are analyzed.By analyzing the survey data and comparing the mileage,driving time and vehicle performance,the paper concludes that the traffic network efficiency has been significantly improved.Through comparative analysis,it is concluded that the increase of the adoption rate of self-driving cars is helpful to reduce urban congestion.