Abstract
Road segment representation is important for evaluating travel time, route recovery and traffic anomaly detection. Recent works mainly consider topology information of road network based on graph neural network, while dynamic character of topology relationship is usually ignored. Especially, the relationship between road segments is evolving with time elapsing. To obtain road segment representation based on dynamic spatial information, we propose a model named temporal and spatial deep graph infomax network (ST-DGI). It not only captures road topology relationship, but also denotes road segment representation under different time intervals. Meanwhile, the global traffic status/flow will also affect local road segments’ traffic situation. Our model would learn the mutual relationship between them, with maximizing mutual information between road segment (local) representation and traffic status/flow (global) representation. Furthermore, it would make road segment representation more distinguishable by this kind of unsupervised learning, and be helpful for downstream application. Extensive experiments are conducted on two important traffic datasets. Compared with the state-of-the-arts models, the experiment results demonstrate the superior effectiveness of our model.
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Notes
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i.e., living street, motorway, motorway link, primary, primary link, residential, secondary, secondary link, service, tertiary, tertiary link, trunk, trunk link, unclassifie.
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Acknowledgment
This work is supported by the National Natural Sci- ence Foundation of China (61902438, 61902439, U1811264, U19112031), Natu- ral Science Foundation of Guangdong Province under Grant (2019A1515011704, 2019A1515011159), National Science Foundation for Post-Doctoral Scientists of China under Grant (2018M643307, 2019M663237), and Young Teacher Training Project of Sun Yat-sen University under Grant (19lgpy214,19lgpy223).
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Liu, W., He, J., Wang, H., Zhu, H., Yin, J. (2021). A Novel Road Segment Representation Method for Travel Time Estimation. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021 International Workshops. DASFAA 2021. Lecture Notes in Computer Science(), vol 12680. Springer, Cham. https://doi.org/10.1007/978-3-030-73216-5_27
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