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The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate short-term passenger flow prediction plays an important role in transit planning and operation. Existing research is mostly based on a joint modelling approach in which transit demand is predicted in an aggregated manner taking the overall passenger flow as input. A critical problem for the joint modelling approach is that the complexity of passenger flow composition and the distinct behavioural response to influential factors are missing out. To address this challenge, this paper proposes a separate modelling approach for passenger flow prediction based on behavioural patterns. To this end, we develop a novel hybrid decision tree (HDT) model coupled with a decision tree model and time series model. The upper layer is a decision tree model, in which the dataset is divided according to passenger types and influential factors, while the lower layer is the time series model achieved by the recurrent neural network. Particularly, this research first undertakes passenger classification using smartcard data through cluster analysis, from which the correlation between the classified passenger flow and influential factors is obtained. The proposed method is tested in a real-life bus route in Guangzhou, China. We also investigate the impact of passenger classification schemes and the minimum amount of data contained by leaf nodes on the performance of the HDT model. Based on this, we recommend the best classification scheme and the optimal value of the minimum amount of data contained by leaf nodes. Comparisons show that our method outperforms other traditional methods in terms of both prediction accuracy and stability. In addition, our method could also provide the prediction of passenger flow composition, which provides more references for customized bus service design. • A novel hybrid decision trees combining the advantages of machine learning model and time series method. • Our method can handle big data by capturing correlations between the external and internal factors. • As opposed to joint modelling, our method is able to predict passenger flow composition. • The prediction outcome is quite outstanding with MAPE of as low as 5%. [ABSTRACT FROM AUTHOR]