Origin-Destination Matrix Prediction in Public Transport Networks: Incorporating Heterogeneous Direct and Transfer Trips

• Published in: IEEE transactions on intelligent transportation systems pp. 1 - 15
• Main Authors: Tang, Tianli, Mao, Jiannan, Liu, Ronghui, Liu, Zhiyuan, Wang, Yiran, Huang, Di
• Format: Journal Article
• Language: English
• Published: IEEE 05-09-2024
• Subjects: Data models; Deep learning; Feature extraction; Graph convolutional networks; heterogeneous graph; origin-destination matrix; Predictive models; public transport; Roads; spatio-temporal feature; Wireless fidelity
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2024.3447611

The efficient operation of urban bus networks largely depends on optimised scheduling conducted before the one-day operation, crucially relying on reliable origin-destination (OD) information. Passengers travel on direct and transfer trips due to complex infrastructure and services in bus networks. These two differential behaviours necessitate a model that captures topological differences to accurately predict the OD matrix. Responding to this need, we propose a graph-based deep learning model, termed the Direct-Transfer Heterogeneous Graph Network (DT-HGN). This model is designed to predict the OD matrix whilst expressly distinguishing direct and transfer passenger behaviour. DT-HGN articulates direct and transfer trips as distinct graphs, each characterised by its unique adjacency matrix. The model's architecture embraces two principal blocks: a Spatio-Temporal (ST) construct and an Auto-Encoder (AE) component. The ST-block applies a Gated Recurrent Unit model and a Graph Convolutional Network to discern features of direct and transfer trips, considering both temporal and spatial dimensions. Conversely, the AE-block utilises a heterogeneous graph convolutional network to transmute the two heterogeneous graphs into latent features. Our real-world validation process, executed over a two-month period on an urban bus network, attests to DT-HGN's robust ability in accurate OD matrix prediction, outperforming contemporaneous state-of-the-art models. This study addresses the crucial need for a comprehensive network-level OD matrix and provides a new perspective for optimising the entire public transport network by accurately depicting station-to-station demand. The approach extends beyond the limitations of traditional bus lines, allowing for a more comprehensive analysis and improvement of urban public transport systems.