Continuous spatial-temporal convolutional networks based neural control differential equations for traffic forecasting

Zengqiang Wang; Di Zang

doi:10.1117/12.3015688

20 February 2024 Continuous spatial-temporal convolutional networks based neural control differential equations for traffic forecasting

Zengqiang Wang, Di Zang

Author Affiliations +

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130642W (2024) https://doi.org/10.1117/12.3015688
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

The Neural Controlled Differential Equation (NCDE) elegantly fuses dynamical systems with deep learning, unveiling profound potential for time series modeling. Harnessing the power of neural networks to sculpt the vector fields inherent to differential equations, this methodology introduces a seamless perspective for emulating spatial-temporal dynamical paradigms. In our research, the NCDE serves as the foundational architecture. Within this construct, we adeptly integrate both Temporal Convolutional Networks (TCNs) and Graph Neural Networks (GNNs) into a framework of continuous state representation to capture long-term spatial-temporal dependencies. This avant-garde modeling approach illuminates new avenues for nuanced modeling of spatial-temporal traffic dynamics, markedly augmenting the fidelity of traffic forecasting. Empirical evaluations conducted on three publicly accessible traffic flow datasets further underscore the superior efficacy of our proposed model in traffic forecasting.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zengqiang Wang and Di Zang "Continuous spatial-temporal convolutional networks based neural control differential equations for traffic forecasting", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130642W (20 February 2024); https://doi.org/10.1117/12.3015688

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KEYWORDS

Differential equations

Neural networks

Data modeling

Deep learning

Diffusion

Education and training

Dynamical systems

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