Research On Time Differential Price Optimization Of Urban Rail Transit Based On Characteristics Of Passenger Flow

In recent years,with the continuous acceleration of urban construction,the network of urban transportation systems has become increasingly perfect.Compared with other modes of transportation,urban rail transit has the advantages of large volume,rapid and punctuality,and plays an important role in the overall urban infrastructure,effectively alleviating urban traffic congestion and other issues.But at the same time,the development of rail transit also faces some problems.During the peak period,due to the large number of passengers traveling and overcrowding in the train,the urban rail transit is in an overloaded state;while the low-peak period has fewer travellers,the travel time is scattered,and the urban rail transit resources are not fully utilized.Therefore,there is an urgent need for an effective measure to guide passengers to adjust travel time and travel choice behavior,so that passenger flow at different peaks and peaks can be more balanced,reduce the disadvantages caused by rail traffic congestion,and effectively improve rail transit to a certain extent.Carrying utilization.The effect of dynamic fares on passenger flow regulation from the perspective of high and low peak time differential pricing will be discussed in this paper.Firstly,according to the temporal and spatial characteristics of urban rail transit,this paper applies the theory of spatio-temporal network to the description of urban rail transit time-sharing pricing,improves the spatio-temporal network,and constructs a time-space network model of urban rail transit considering time-sharing pricing.The effect of different fares on the distribution of passenger flow at different times,that is,the choice of the same OD for the next traveler.At the same time,based on the improved Dijkstra algorithm and the urban space-time space-time network considering the time-division,a search algorithm for effective spatio-temporal path is proposed.Secondly,this paper will consider the time-of-day pricing of urban rail transit space-time network into five processes:Internet access,operation,parking,transfer,and offline.Based on the fare,time,congestion cost and penalty time from morning to night,the generalized expenses on each road segment are defined.Because the demand of different time periods is random,this paper builds a multi-period elastic demand random passenger flow allocation model under urban rail transit spatio-temporal network based on multi-layer Logit model and elastic demand stochastic user equilibrium theory,and proposes a corresponding algorithm.The model and algorithm are verified by a simple example.The results show that the time-sharing pricing has a certain adjustment effect on the passenger flow distribution of urban rail transit space-time networks under different time periods.Finally,this paper analyses the relevant factors affecting the time differential pricing policy,and constructs a bi-level programming model.From the perspective of traffic demand managers,time-sharing pricing is used to alleviate the phenomenon of passenger congestion in peak hours.The upper-level programming considers the minimization of the matching value between capacity and volume and the maximization of the enterprise operating cost ratio as the optimization objectives,and uses the conversion coefficient to deal with multiple objectives;the lower-level programming is a stochastic equilibrium allocation model of multi-period elastic demand.For the optimization goal in the bi-level programming model,the genetic algorithm is used to solve it,and the model and algorithm are verified by a numerical example.The results show that it is more reasonable to consider the two objectives in a comprehensive way than to consider only one objective for fare optimization.At the same time,comparing different time-sharing fare schemes,it is found that it is more effective to improve the peak-time fare and reduce the low-peak-time fare for alleviating the congestion in peak-time.