Research On Peak-congestion Control And Management For Urban Rail Transit Network

Passenger over-crowding of urban rail transit is a prominent matter in big cities,which seriously reduces passenger service level and causes operational safety hazards.Commuters are the absolute main component of all the passengers in peak hours,with such "rigid" characteristics of high intensity in short time,prominent flow direction,temporal and spatial stability in travel pattern,which makes it very difficult to relieve the congestion in peak hours.Therefore,starting with the nature of passenger congestion(contradiction between supply and demand)and combining the current operation management practices of urban rail transit in domestic and foreign,two forms of congestion relief strategies from the demand side are studied in this paper in order to improve the passenger organization level,risk prevention and control capability,which are inflow control and time differential pricing.The detail of study contents are as follows:(1)Commuters identification and feature mining:A rule-based commuters identification algorithm is constructed based on the internal relation of travel information extracted from automatic fare collection system(AFC)data.The commuters'travel characteristics from the aspects of principal composition features,spatial and temporal distribution,and "rigid" stability in travel pattern are explored;that passenger congestion is highly cyclical and repetitive on the location,time and duration are found by matching transportation capacity and passenger flow volume in morning peak;in the end,all congestion relief measures and their application conditions are summarized from both supply and demand perspectives,which provides the theoretical foundation for congestion-relief policies.(2)The station inflow control(SIC)scheme generation for large-scale urban rail transit network:A station inflow control algorithm based on transportation capacity bottlenecks elimination strategy to generate a static regular inflow control scheme in peak hours in normal weekdays for large-scale urban rail transit network is proposed,which has four key steps:flow relationship construction,transportation capacity bottleneck identification,transportation capacity bottleneck feedback and elimination,and inflow control scheme generation.A SIC generation system based on the proposed method has been developed,which provides a tool support for the actual cooperative SIC generation to relieve peak congestion,and the Beijing subway is taken as the object for empirical analysis to verify the effectiveness,accuracy and high efficiency of the proposed algorithm.(3)Research on passenger travel behavior after the implementation of time differential pricing:The rail transit passengers are classified five groups by unsupervised clustering method base on classification indexes constructed in perspective of "consumer behavior" by using AFC data within a continuous period of time.The departure time transfer elasticity by group is measured based on tracing the AFC data before and after the trial pre-peak morning 50%discount price policy in Beijing subway,and the results shows that the transfer elasticity of each passenger group decreased sharply with the increase of the length of transfer time interval needed for enjoying discount price,and the maximum acceptable length of transfer time interval needed for enjoying 50%discount policy for passengers is about 30 minutes,which provides basic parameters for future expansion of time differential pricing on the Beijing subway network.(4)The mathematical model to formulate the discount pricing scheme is constructed to relieve peak congestion:The feasibility of discount pricing strategy is analyzed,and a universal model of discount pricing scheme formulation is constructed based on nonlinear integer programming method,with the model objective to comprehensively consider the acceptable loss in revenue and the relief of the severe section congestion,which informs decision-making for the selection of discount stations,discount value and discount time interval.Finally,taking Line BT of Beijing subway as an empirical study,the accuracy and effectiveness of model is verified.