Research On Congestion Propagation Evolution And Traffic Limiting Control Of Peak Passenger Flow In Urban Rail Transit Network

In recent years,China’s urban rail transit network has been developing rapidly.The scale of rail transit networks in major cities has been expanding,and network operation has become an inevitable trend in the development of urban rail transit.The networked rail transit attracts a large number of passengers,especially during the peak hours,which leads to the frequent occurrence of congestion.The impact of peak passenger congestion is no longer limited to a single station,but gradually spreads to other stations and lines with the operation of rail trains,and then affects the whole rail transit network,which has become the main problem of urban rail transit network operation.Therefore,it is an effective measure to reduce the impact of peak passenger congestion on the rail network by studying the model of peak passenger congestion propagation in the rail network to provide a basis for the rail operation management to grasp the evolution of the congestion propagation process and to adopt flow control measures for peak passenger congestion propagation.The main research contents and findings of this thesis are as follows:(1)The complex network theory was introduced to analyze the urban rail transit network,summarizing that the urban rail transit network has three network characteristics: line network scale growth,priority connectivity of key stations and regularity of degree values,using Space L modeling method to construct the urban rail transit network topology model and calculate the static state geometry and statistical characteristic values.Taking the topology model of Chongqing rail transit network as an example,it was found out that its rail network degree and Degree distribution conformed to the power-law distribution,and had a small average shortest path length of the network,which proved that Chongqing rail network had complex network scalefree characteristics and small-world network characteristics.(2)The rail transit IC card data and mobile payment data were cleaned and merged into the same valid data structure.Through the method of passenger flow accumulation between sections,the section passenger flow data was obtained using programming language,and the spatiotemporal distribution characteristics of peak passenger flow were analyzed based on the processed data.Based on the definition and causes of peak passenger congestion,the propagation characteristics of peak passenger congestion were discussed,and the propagation evolution under different types of congestion propagation was depicted.(3)Combining the complex network virus propagation model and the theory of metacellular automata,the SIRS-CA model of urban rail transit peak passenger congestion propagation was constructed,which can portray the change of congestion state of individual nodes in the rail network and the macroscopic evolution process of congestion propagation,taking into account the situation that congestion occurs again after the congested nodes return to normal.Based on the passenger flow data of Chongqing urban rail transit network,the simulation evolution analysis of 2 hours in the morning peak was carried out,and the propagation impact time of Chongqing rail transit network was 96 min,the maximum propagation impact range was 21 platform nodes,and the high incidence of congestion was concentrated in the period from 8:00 to8:36.The simulation process also found that two nodes were congested again after restoring the normal state,which initially verified the accuracy of the model.(4)A peak passenger congestion cooperative flow restriction control method based on the identification of key nodes in the rail network was proposed.By identifying the key nodes in the rail network that had a greater impact once the congestion occurs,the upstream and downstream stations could be controlled cooperatively to block and suppress the congestion propagation situation.The application analysis was carried out in Chongqing rail transit,and it was found that the duration of congestion in Hongqihegou station,where node 42 was located,was reduced by 3/4 after the collaborative flow restriction control is applied to one of the identified key nodes.The duration of congestion in both upstream and downstream stations was significantly reduced,which initially verifies the effectiveness of this multi-station collaborative flow restriction method.This thesis constructs a SIRS-CA model of urban rail transit peak passenger congestion propagation based on the static line network of complex urban rail transit network and dynamic data of multi-source passenger flow,and proposes a multi-station collaborative flow restriction control method based on key node identification,which solves the problem of mismatch between urban rail transit networked demand and service capacity during peak hours to a certain extent,and improves the rail transit network passenger flow congestion propagation and passenger flow.It also provides a reference basis for the rail transit operation management department to study and develop the peak passenger flow congestion control method.