Abstract
Providing accurate travel time prediction plays an important role in Intelligent Transportation System. It is critical in urban travel decision making and significant for traffic control. The main limitation of existing studies is that they do not fully consider the spatiotemporal dependence, exogenous dependence and dynamics of travel time prediction. In this paper, we propose a deep learning model, called DLSF-GR, based on graph neural networks and recurrent neural networks for travel time prediction, which combines multiple learning components to improve learning efficiency. We evaluate the proposed model on the real-world trip dataset in China by comparing with several state-of-the-art methods. The results demonstrate that the developed model performs the best in terms of all considered indicators compared to several state-of-the-art methods, and that the developed specified cross-validation method can enhance the performance of the comparison methods against to the random cross-validation method.
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Acknowledgements
This study was supported in part by the National Natural Science Foundation of China (Nos. 72171161, 71971041, and 71871148); by the China Scholarship Council (No. 2022M710626)； by the Major Program of National Social Science Foundation of China (Nos. 20&ZD084, 21&ZD120, and 18ZDA058); by the Key Research and Development Project of Sichuan Province (No., 2023YFS0397); by the Chengdu Philosophy and Social Science Planning Project (No., 2022C19); and by the Sichuan University to Building a World-class University (No. SKSYL2021-08).
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Wang, D., Zhu, J., Yin, Y. et al. Dynamic travel time prediction with spatiotemporal features: using a GNN-based deep learning method. Ann Oper Res 340, 571–591 (2024). https://doi.org/10.1007/s10479-023-05260-2
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DOI: https://doi.org/10.1007/s10479-023-05260-2