Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution

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Origin–destination (OD) flow prediction is crucial for predicting inter-station passenger flows in intelligent transport systems. However, previous OD prediction methods have ignored the delay of OD flows and failed to focus on the supplementary effect of the arrival OD flow (Out-OD) flow data on OD flows. We innovatively propose an OD flow prediction method based on Bidirectional Attention and Interconnected Feature Evolution (BiST-IF) to address these challenges. Firstly, we propose a correction method based on period delay probability and real-time flow features for OD flow information. Furthermore, for the flow information of different periods, we design a bi-directional attention module to achieve the preliminary prediction by deconstructing and analyzing the temporal and flow features of OD flow and fusing the temporal and spatial features. After that, we design the mutual information module of Out-OD and OD flow, combining it with the new gating algorithm to enhance the periodic features. Finally, the prediction results from the periodic pattern and the temporary fluctuation of OD flow are fused to obtain the OD flow prediction results. Extensive experiments on two real-world datasets show that the prediction performance of BiST-IF significantly outperforms other state-of-the-art models. Specifically, BiST-IF improves the MAE and RMSE by an average of 7.55% and 7.31% on the HZMetro dataset, and 5.77% and 2.33% on the NYC-TOD2018 dataset, respectively, compared to the best baseline approach.