STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

Authors

  • Jiahao Ji State Key Laboratory of Software Development Environment, School of Computer Science & Engineering, Beihang University, Beijing, China
  • Jingyuan Wang State Key Laboratory of Software Development Environment, School of Computer Science & Engineering, Beihang University, Beijing, China Peng Cheng Laboratory, Shenzhen, China
  • Zhe Jiang Department of Computer & Information Science & Engineering, The University of Florida
  • Jiawei Jiang MOE Engineering Research Center of Advanced Computer Application Technology, School of Computer Science & Engineering, Beihang University, Beijing, China
  • Hu Zhang MOE Engineering Research Center of Advanced Computer Application Technology, School of Computer Science & Engineering, Beihang University, Beijing, China

DOI:

https://doi.org/10.1609/aaai.v36i4.20322

Keywords:

Data Mining & Knowledge Management (DMKM)

Abstract

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.

Downloads

Published

2022-06-28

How to Cite

Ji, J., Wang, J., Jiang, Z., Jiang, J., & Zhang, H. (2022). STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4048-4056. https://doi.org/10.1609/aaai.v36i4.20322

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management