| With the aggravation of urbanization in China,the volume of passengers continues to rise in urban rail transit,and passenger over-crowing is becoming more and more serious,which leads to the increase of travel delay and the decrease of travel comfort.The operators of urban rail transit generally take measures such as increasing transportation capacity and limiting station flow to limit passengers in peak periods,but they do not fundamentally reduce the travel demand in peak periods.Scholars all over the world generally believe that the dynamic and diversified ticket strategy can guide passengers effectively and achieve the goal of " peak-cutting and traffic balancing",which is an effective means to alleviate the passenger congestion.At the same time,the analysis of passenger-data shows that there are not only peaks and troughs throughout the day,but also obvious peaks and troughs during the peak period.The congestion during the peak period will be transmitted to the trough,which provides the possibility to alleviate the congestion by balancing the passenger during the peak period.This paper introduces a ticket reward strategy considering user virtual account based on the research on the characteristics of peak passenger flow.The strategy guides passengers to adjust their departure time and take different trains,so as to achieve the purpose of balancing peak passengers and easing the congestion.The main contents of this paper include the following four aspects:(1)This paper analyzes the connotation of ticket incentive in urban rail transit and puts forward the implementation rules and design guidelines of ticket reward strategy based on the congestion charging theory.Taking Beijing Subway as an example,this paper analyzes the distribution characteristics and adjustability of peak passengers by using the time-space imbalance index,passenger volume index and congestion index,makes clear the feasibility and necessity and puts forward the applicable scenarios of the strategy.(2)Based on the traffic bottleneck theory,the bottleneck model of urban rail transit peak passengers is established.The principle of user equilibrium(UE)for time-sharing passengers allocation is considered,the identification method of urban rail transit passenger flow bottleneck is proposed,and single OD numerical example is designed for verification in this paper.The results show that the peak-bottleneck model can reduce the queuing delay by 5% and the total cost of the system by 1.8%,which shows that the model is effective.But its system efficiency is small,and it can't eliminate the peaks and troughs during the peak period,so other means are needed to cut the peaks and fill the troughs.(3)This paper introduces a ticket reward strategy to establish a bi-level planning model for peak passenger regulation.The upper model is the operator benefit maximization model considering diminishing marginal cost,and the lower model is the peak passenger-bottleneck model considering user equilibrium(UE).A heuristic algorithm was designed to solve the problem.Particle swarm optimization(PSO)is taken to determine the fare increase ratio in the upper model,while continuous average method(MSA)is taken to obtain the reward ratio in the lower model,passenger distribution,queuing time and system cost under the optimal fare increase ratio are obtained through repeated iterations.(4)Based on the partial line network and historical passenger data of Beijing Subway Line 13,a multi-OD example was designed to verify the validity of the model and algorithm.The results show that the average queuing time of the system is significantly reduced,the overall queuing time of passengers is reduced by 75.9%,the total cost of the system is reduced by 26.3% after optimization.So the introduction of the ticket incentive strategy to balance the peak passenger has an obvious effect.This paper expands the theory and method of rail transit passenger-bottleneck,which can provide reference for operators to formulate ticket strategy for peak passenger flow allocation... |
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