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The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model. [ABSTRACT FROM AUTHOR]

• Spatiotemporal features of passenger flow under station closures is considered. • Residual time series due to station closures is checked and corrected. • The changes in passenger flow during station closures is captured. • EDFM-SVR hybrid approach outperforms regular approach. Passenger flow prediction is critical for subway managers to efficiently organize passenger flow and assign capacity resources. Station closures caused by economic conferences and political events alter the topology of subway networks, resulting in large-scale passenger flows with irregular trends. Thus, predicting subway passenger flow during station closures is a challenging task. In this study, we propose a hybrid model for predicting short-term passenger flow during subway station closures. First, a spatiotemporal ensemble prediction model is used to capture the temporal characteristics of passenger flow at each station and the spatial characteristics of passenger flow among distinct groups of stations. Then, to address the effects of station closures, a support vector regression method is devised to investigate the residual time series from the ensemble model. Finally, a case study of Beijing subway is examined to validate the performance of the proposed model. The results indicate that the proposed model is superior to other state-of-the-art models in terms of accuracy and reliability in estimating the effects of station closures. Furthermore, the residuals from station closures are thoroughly converted into white noise and the proposed model is more interpretable than those in previous studies. [ABSTRACT FROM AUTHOR]

• Three factors are ascertained to measure the effects of disruptions. • Real-time prediction requirements are particularly considered in the model. • The model shows high accuracy in predicting the effects of disruptions. • The model shows strong generalizability on two different high-speed railway lines. Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L , N , and T , respectively. [ABSTRACT FROM AUTHOR]

• Deep learning models were employed to predict train delays. • Train operations were modeled as sequences. • Interactions were captured from train groups in the prediction model. • The proposed model shows satisfactory performance on different railway lines. This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models. [ABSTRACT FROM AUTHOR]