TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction

Bus network plays a critical role in urban transportation affecting the use of private vehicles, traffic congestion, and urban accessibility. The accurate prediction of bus passenger flow is key to improving transit passenger experience and increasing the efficiency of bus network operations. In line with recent advances in deep learning for passenger flow prediction, graph neural networks (GNNs) have become increasingly popular due to their ability to account for the network structure between stops. Existing GNN-based models for bus passenger flow prediction, however, face several limitations. First, they do not take into account some distinctive characteristics of bus networks, such as their coexistence with vehicular traffic and their high sensitivity to urban traffic conditions. Moreover, sequence prediction models that have been widely applied to multistep passenger flow prediction suffer from a critical issue, called “exposure bias.” This results in the propagation and accumulation of errors through prediction steps while making predictions for farther time horizons. To address these issues, this study presents the Traffic-Aware multistep Graph Neural Network (TMS-GNN) model with Scheduled Sampling, a graph-based deep-learning framework designed to forecast multistep bus passenger flows at individual stops across a bus network. The model takes into account factors such as bus stop connectivity, urban traffic impacts, and multi-dimensional temporal patterns; and addresses exposure bias by employing a curriculum learning strategy called Scheduled Sampling. The comparison between the proposed model and other popular baseline models on two real-world networks with different geographical and urban patterns in Canada and USA shows that TMS-GNN outperforms the baselines in both the overall network-wide task, as well as multistep prediction. Also, to verify the contribution of the proposed components of the model, an ablation study is conducted. The results of the ablation study validate the design choices as well. • Built traffic-aware graph model for bus passenger flow, reflecting real-world dynamics • Introduced robust multi-step prediction to reduce error accumulation over time steps • Proposed fusion module to capture multi-scale temporal trends in flow predictions • Demonstrated robustness via tests on two datasets, outperforming existing models

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