A Noise-Robust Approach Using Dynamic Graph Neural Networks for Bus Passenger Flow Prediction

Short-term passenger flow prediction is critical for intelligent scheduling and efficient operation of public transportation systems. However, existing methods often struggle to maintain robustness and generalizability when facing complex and heterogeneous noise sources, such as passenger flow noise including sensor-induced missing values and abrupt ridership fluctuations caused by unexpected events, and graph structural noise resulting from dynamic changes in the network topology due to route modifications or stop closures. To address these challenges, this paper proposes a novel deep learning framework: Robust Dynamic Graph Neural Networks (RDGNN), that integrates noise cleaning, dynamic graph modeling, and more efficient prediction. The proposed noise cleaning employs K-Nearest Neighbor imputation and Gaussian Mixture Models with a penalty function to mitigate data-level noise, while a graph structure denoising module is introduced to correct topological anomalies in the transit network and enhance the reliability of spatial representation. For feature modeling, the framework constructs a dynamic graph neural network from a subgraph perspective to capture both spatial dependencies and temporal dynamics, particularly suited to modeling interactions among transfer stations. A Liquid Neural Network is adopted as the prediction module, leveraging its strong adaptability and memory capacity to handle irregular and non-stationary time series, while remaining computationally efficient. The RDGNN model was trained and validated on real-world bus passenger flow data from Ames, Iowa, and further evaluated on the large-scale EXO dataset. Compared to state-of-the-art models, RDGNN consistently achieves better prediction accuracy, higher robustness to noise and missing data, and greater computational efficiency. Its strong and stable performance across both datasets, including in scenarios such as route disruptions and seasonal variation, demonstrates excellent generalization capability in diverse urban transit systems. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XinyiZhou0318/A-Noise-Robust-Approach-Using-Dynamic-Graph-Neural-Networks-for-Bus-Passenger-Flow-Prediction</uri>

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