Research On Train Interval Optimization Of Urban Rail Transit Based On Long And Short Traffic Route Mode

As the urban rail transit improves by leaps and bounds,an increasing number of passengers prefer to use urban rail transit for trip.On account of this,the passenger flow distribution of rail transit lines in many cities has become more and more unbalanced.The past single routing scheme is often accompanied by the disadvantages of uneven load rate,crowded space in large passenger flow section and low utilization rate of trains in small passenger flow section.By adopting the service mode,the overall passenger waiting time can be reduced and the overall service quality can be effectively improved.Therefore,this paper is committed to putting forward a scientific and reasonable model to formulate the operation scheme of long and short routing trains,so as to further optimize the train interval.The main contents of the study include the following parts:(1)Put forward the setting strategy of train routing based on k-means algorithm(k-means clustering algorithm),calculate the number of operating vehicles under different routing types,and verify the effectiveness and feasibility of the scheme.On the train operation route of Hangzhou Rail Transit Line 2,introduce the basic information of the line and the distribution of the transform of passenger flow,obtains the train routing scheme of Hangzhou Rail Transit Line 2 through the cluster analysis of passenger flow,and finally compares the balance of the number of operating vehicles and full load rate of each scheme by changing the position of turn back station,the effectiveness and enforceable of the routing scheme are verified.(2)This paper focuses on the concept,principle and implementation process of Fruit Fly Optimization Algorithm(FOA);to improve the related shortcomings of FOA,analyzed the convergence performance,performance parameters,search mechanism,parameter setting and operation steps,and use the standard test function for the standard FOA and Improved Fruit Fly Optimization Algorithm(IFOA)of search accuracy,global search performance and efficiency.The results indicate that compared with the standard FOA,the convergence speed and convergence accuracy of the IFOA have been greatly enhanced.(3)Based on the form of large and small routes,the train headway optimization model of urban rail transit is established with the train headway of large and small routes as the decision variable,the upper and lower limits of train headway and the number of trains used as the constraints,and the optimization model takes the minimum train running kilometers and the minimum waiting time of all arriving passengers as the target function.Multi-objective optimization problem is transformed into a single-target problem by a linear weighted sum method for analysis,and the model is solved using an IFOA.Finally,Hangzhou Rail Transit Line 2 is taken as an example to verify the effectiveness and feasibility of the model.