Abstract
Vehicle-based travel time estimation is crucial for many travel scheduling and city planning applications in intelligent transportation systems. Since trajectories in different trips are affected by evolutionary Spatio-temporal dynamics (e.g., evolving travel patterns for different days of the week and varying road networks affected by traffic accidents or temporary restrictions, etc.), it is substantial to investigate these dynamics for accurate estimation. In this paper, we propose a novel deep learning model which fuses location features, distance features, and temporal features with meta learning-based neural networks, to implicitly learn path representations for evolving travel patterns in different days of week and road networks. Specifically, we utilize the meta learning-based optimization method to transfer the shared meta knowledge across trajectories in distinct Evolving-Tasks (i.e., a limited amount of trajectory data on different days of the week), which facilitates generalizing rapidly on evolving travel patterns for different days of the week. In addition, road network information is obviated in our model, which makes it a natural solution to tolerate evolving road networks while mitigating the computation burden contemporaneously. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.
This work was supported in part by the National Natural Science Foundation of China under Grant 62261042, the Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation and the Fengtai Rail Transit Frontier Research Joint Fund under Grant L221003, Beijing Natural Science Foundation under Grant 4232035 and 4222034, the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA28040500, the China Postdoctoral Science Foundation under Grant 2024M750200 and BUPT Excellent Ph.D. Students Foundation under Grant CX2022132.
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Wang, C., Zhao, F., Luo, H., Fang, Y., Zhang, H., Xiong, H. (2024). Vehicle-Based Evolutionary Travel Time Estimation with Deep Meta Learning. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15024. Springer, Cham. https://doi.org/10.1007/978-3-031-72356-8_17
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