Passenger flow prediction for citywide bus stops based on gated multi-graph convolutional network
Document Type
Journal Article
Publication Date
2026
Subject Area
place - urban, mode - bus, operations - frequency, operations - scheduling, infrastructure - stop, ridership - demand
Keywords
bus stop, gated recurrent unit, multi-graph convolutional network, passenger flow prediction
Abstract
Accurate passenger flow prediction at bus stops is the basis of bus operations. Capturing the temporal, spatial and periodic correlations to improve the prediction accuracy of passenger flow efficiently is one of the main problems faced by bus-stop passenger flow prediction. In this study, a deep-learning framework that combines a graph-convolution network (GCN) and gated-recurrent unit (GRU) is proposed to predict citywide bus-stop passenger flow. First, to extract the spatial correlation of passenger flow, a method is proposed to construct a multi-semantic graph according to the topological attributes of the stop to capture deep spatial correlations. Second, the proposed framework considers the periodic correlation of passenger flow, which is usually neglected and differs from the temporal correlation. Finally, a bus-stop passenger flow prediction framework is constructed using a GCN to capture spatial correlations and a GRU to capture temporal and periodic correlations. The proposed framework was tested on real datasets. The results show that for time granularities of 30, 45, and 60 min, the root-mean-square errors (RMSE) yielded by this model were 2.2275, 2.7832, and 3.2902, respectively. Compared with the baseline models, the average RMSE was reduced by 21.98%, 27.31%, and 29.25%, respectively. The multi-semantic graph and periodic correlation proposed in this study improve the prediction accuracy of citywide passenger flow, and the fitting results of selected cases show that the model is robust. The results can provide data support for scheduling plan and departure frequency of urban public transport enterprises.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Wang, Y. Y., Zhang, C., Qu, W. P., & Jiang, Z. H. (2026). Passenger flow prediction for citywide bus stops based on gated multi-graph convolutional network. Journal of Intelligent Transportation Systems, 30(5), 1105-1118.
