Optimization Of Intercity Railway Train Stopping Scheme Considering Passengers’ Fast Travel Demand

The intercity railway mainly serves the commuting passengers between the core cities and the sub-central cities within the urban agglomeration and the demand for rapid travel is relatively prominent.As the process of integration of urban agglomerations continues to advance,the economic links between core cities become closer and closer,and the demand for inter-city commuting passenger flow gradually increases.With the coordinated development of urban agglomeration,the demand for rapid passenger travel of inter-city railway is becoming more and more prominent.Based on the above background,this paper studies the optimization of intercity railway train stopping scheme considering passengers’ fast travel demand.The main research contents and conclusions are as follows:.(1)Based on the existing research,the advantages and disadvantages of the four train stop modes of the intercity railway as well as the influencing factors of the stop scheme plan are analyzed.Based on the number of trains stops,the rate of node stops,the number of stopping modes,the ratio of OD for fast travel and the average stopping time index of train intermediate stations,the characteristics of stop scheme plans for typical intercity railways are studied.(2)This paper describes the degree of realization of passengers’ fast travel demand under the stopping scheme with the realization rate of passengers5 fast travel demand,and constructs a multi-objective optimization model of train stopping scheme aiming at minimizing the total travel time of passengers,maximizing the realization rate of passengers’ fast travel demand’ and minimizing the operation cost of enterprises.The linear weighting method is used to transform it into a single-objective optimization problem and a corresponding genetic algorithm is designed to solve the mode.Taking Changsha-Xiangtan-Zhuzhou intercity railway as an example,the validity of the model and the algorithm is verified,and the sensitivity analysis of the stopping rate of nodes and the proportion of exchange passenger flow between primary nodes are carried out.Under the parameters set in this paper,the total travel time,the realization rate of fast travel demand and the operation cost of Changsha-Zhuzhou-Xiangtan intercity railway is optimized by 9.74%,230.30%and 22.22%respectively.The validity of the model and the algorithm are verified.The results show that the smaller the stopping rate of nodes,the better the stopping scheme optimization effect.Other parameters are the same,when the node stopping rate is the smallest,the optimization rate of total passenger travel time,fast travel demand realization rate and enterprise operation cost of Changsha-Xiangtan-Zhuzhou intercity railway optimization scheme is 6.15%,48.78%and 15.87%higher than that of the maximum node stopping rate,respectively.The results show that the larger the proportion of exchange passenger flow between advanced nodes,the higher the optimization rate of total passenger travel time and fast travel demand.Other parameters are the same,when the proportion of exchanged passenger flow among advanced nodes increases by 10%,the optimization rate of total travel time and fast travel demand is 1.12%and 23.39%higher than that of the original passenger flow structure,respectively.(3)Based on the constraints of one-stop transfer,an optimization model of train stopping scheme is constructed,which aims at minimizing the total travel time of passengers,maximizing the realization rate of passengers5 fast travel demand and minimizing the operation cost of enterprises,and a genetic algorithm is designed to solve the problem.Taking the Changsha-Xiangtan-Zhuzhou intercity railway as an example to verify the effectiveness of the model and algorithm.The results show that under the parameters set in this paper,the total travel time,the realization rate of fast travel demand and the operation cost of Changsha-Zhuzhou-Xiangtan intercity railway is optimized by 9.85%,228.03%and 22.22%respectively.