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Deep Learning-Driven Bus Short-Term OD Demand Prediction via a Physics-Guided Adaptive Graph Spatio-Temporal

Zhichao Cao^1,2, Longfei Song¹, Silin Zhang¹

¹School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This summary is machine-generated.

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This study introduces the physics-guided adaptive graph spatio-temporal attention network (PAG-STAN) for predicting bus origin-destination (OD) demand. The model achieves superior accuracy by integrating physics-guided mechanisms and attention networks.

Area of Science:

Transportation Science
Artificial Intelligence
Deep Learning

Background:

Accurate short-term bus origin-destination (OD) demand prediction is crucial for public transit operations.
Existing models often struggle with limited data and capturing complex spatiotemporal dependencies.
A novel deep learning approach is needed to enhance prediction accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a novel deep learning model, PAG-STAN, for short-term bus OD demand prediction.
To improve prediction accuracy using a physics-guided loss function and attention mechanisms.
To validate the model's performance on a small-scale dataset with a 30-minute interval.

Main Methods:

Constructed a modified deep learning model (PAG-STAN) integrating physics-guided mechanisms, adaptive graph convolution, attention networks, and a spatiotemporal encoder-decoder.

Keywords:

PAG-STAN deep learning short-term OD demand prediction small-scale bus dataset

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Employed an encoder-decoder architecture with adaptive graph convolutional LSTM (AGC-LSTM) and a multi-head attention mechanism.

Utilized a masked physics-guided loss function incorporating boarding passenger volume and OD demand, trained with Adam optimizer and early stopping.

Main Results:

PAG-STAN demonstrated superior prediction accuracy compared to other deep learning models.
The model achieved significant reductions in Root Mean Square Error (RMSE) by 6.19%, Mean Absolute Error (MAE) by 6.59%, and Weighted Mean Absolute Percentage Error (WMAPE) by 8.20%.
An improvement of 1.13% in R-squared (R²) was observed, indicating enhanced predictive power.

Conclusions:

The proposed PAG-STAN model effectively predicts short-term bus OD demand, outperforming existing methods.
Integrating physics-guided principles and attention mechanisms enhances the model's ability to capture complex spatiotemporal patterns.
The study highlights the potential of advanced deep learning techniques for optimizing public transportation management.