Passenger flow prediction is important for the operation of urban rail transit. The prediction of abnormal passenger flow is difficult due to rare similar history data. A model based on the fusion of support vector regression (SVR) and long short-term memory (LSTM) neural network is proposed. The inputs of the model are the abnormal features, which consist of the recent real volume series and the predicted volume series based on the periodic features. A two-stage training method is designed to train the LSTM model, which can reflect the large fluctuations of abnormal flow more timely and approximately. A combination method based on the real-time prediction errors is proposed, on which the outputs of SVR and LSTM are combined into the final outputs of the prediction model. The results of the experiments show that the SVR-LSTM model more accurately reflects the abnormal fluctuations of passenger flow, which performs well and yields greater forecast accuracy than the individual models. INDEX TERMS Short-term passenger flow prediction, urban rail transit, support vector regression (SVR), long short-term memory (LSTM).

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This paper is aimed at crowding phenomenon in the subway. As passengers are inclined to choose the route with minimum disutility, we put forward a route choice model which is constructed to achieve minimum objective function of feasibility for the optimal solution. Meanwhile we set passenger volume threshold values according to capacity of facilities. In the case of actual capacity exceeding the threshold, the decision node of constrained route will be selected; computing procedure about searching decision points will be presented. Then we should set rational restrictions at the decision node of the minimum utility function route to prevent too many passengers' access to platform. Through certification, this series of methods can effectively ensure the safety of the station efficient operation. Hindawi Publishing Corporation

In urban mass transit network, when passengers' trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

Time-schedule network with constraints on arcs (TSNCA) means network with a list of pre-defined departure times for each arc. Compared to past research on finding the K shortest paths in TSNCA, the algorithm in this paper is suitable for networks having parallel arcs with the same direction between two nodes. A node label algorithm for finding the K shortest paths between two nodes is proposed. Temporal-arcs are put into the labels of nodes and arranged by ascending order. The number of temporal-arcs is limited to K in every label of node to improve the effectiveness of the algorithm. The complexity of this algorithm is OðjAj log r þ K 2 jAj þ K 2 jNj logðKjNjÞÞ, where r is the maximum number of departure times from a node, jAj is the number of arcs in network, and jNj is the number of nodes in network. Experiments are carried out on major part of real urban mass transit network in Beijing, China. The result proves that the algorithm is effective and practical.