Urban rail transit (URT) is an essential mode of travel used for evacuating passengers from railway stations. To provide timely and safe connecting services for passengers arriving at railway hubs, it is recommended for URT operators to develop a coordinated connection scheme considering the periodic incoming volume of railway passengers. According to the fluctuation characteristics of the large passenger flows in railways, this paper presents a reasonable scheme for increasing the capacity of URT trains. First, a method for calculating the effective evacuation capacity of URT trains and determining the time range that should be optimized according to the transportation capacity matching degree is proposed. The dwell time and departure interval are taken as decision variables in this adjustment period. Two constraints are considered: capacity convergence and the maximum safe capacity of the platform. Based on the consideration of reserved capacity for subsequent sections, the objective is to optimize the degree of matching between the effective evacuation capacity of the URT and the transfer demand of the arriving railway passengers. A multi-objective nonlinear integer-programming model is established for the coordinated connection of URT trains with large passenger flows, and a solution process of a train connection scheme is designed that involves a genetic algorithm. Finally, the effectiveness of the proposed model and algorithm is analyzed and verified by considering the Shanghai Hongqiao Hub-a transfer station between a high-speed railway and URT-as an example. INDEX TERMS Urban rail transit, connection scheme, passenger evacuation, integer programming, genetic algorithm.

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In this study, we developed a method for coordinating and optimizing the train connection plans of different lines under the conditions of urban rail transit (URT) network operation. The method allows trains of different lines to form good connections at transfer stations, which can shorten the waiting time of passengers for transfers and reduce passenger retention. A mathematical model was developed to simulate the interaction between passengers and trains. Two optimization models were developed for the train connection plan of network transfer stations based on different optimization objectives during peak and off-peak hours. Subsequently, a corresponding solution method based on a genetic algorithm and simulation was designed. Finally, the Suzhou URT network was used as a case study, and the passenger flow of the transfer station was simulated and calculated using relevant automatic fare collection (AFC) data. The results indicated that the average waiting time and the number of passengers stranded were reduced using the proposed method. The calculation example demonstrated the effectiveness of the model and algorithm, which can guide the coordinated preparation of a network train connection plan.

Large-scale activities, holidays, and emergencies often cause a significantly large burst of passenger flow demand in some urban rail transit (URT) stations in a short time, called large passenger flow (LPF). The LPF will propagate through the entire URT network of the city. The impact of the frequent occurrence of LPF on network service levels is crucial and unpredictable. This article describes an analysis of how this LPF propagates through the entire network inspired by how radionuclide imaging is done in clinical medicine. In this study, with LPF of URT as the research object, a propagation model of LPF in URT based on AFC data, train operation data, and URT network topology data was developed, which was inspired by the concept of radionuclide imaging in clinical medicine. In the condition of obtaining the list of passenger route selection ratios, the dynamic propagation state matrix of the LPF in the network is solved. The contribution value matrix of the LPF was proposed to evaluate the impact of the LPF on the URT network. Considering the LPF in Chengdu East Railway Station, China, as an example, the propagation effect of LPF in the Chengdu Metro network was analyzed, and the effectiveness of the proposed model was confirmed.