Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation
Paper
Zhang, Tianyu, Li, Lin, Zhang, Rui and Tao, Xiaohui. 2024. "Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation." H., Zhang W.Yang Z.Wang X.Tung A.Zheng Z.Guo (ed.) 8th APWeb-WAIM joint International Conference on Web and Big Data (APWeb-WAIM 2024). Jinhua, China 30 Aug - 01 Sep 2024 Singapore . Springer. https://doi.org/10.1007/978-981-97-7235-3_28
Paper/Presentation Title | Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation |
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Presentation Type | Paper |
Authors | Zhang, Tianyu, Li, Lin, Zhang, Rui and Tao, Xiaohui |
Editors | H., Zhang W.Yang Z.Wang X.Tung A.Zheng Z.Guo |
Journal or Proceedings Title | Proceedings of the 8th APWeb-WAIM joint International Conference on Web and Big Data (APWeb-WAIM 2024) |
Journal Citation | 14962, pp. 421-436 |
Number of Pages | 16 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Singapore |
ISBN | 9789819772346 |
9789819772353 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-97-7235-3_28 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-97-7235-3_28 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-97-7235-3 |
Conference/Event | 8th APWeb-WAIM joint International Conference on Web and Big Data (APWeb-WAIM 2024) |
Event Details | 8th APWeb-WAIM joint International Conference on Web and Big Data (APWeb-WAIM 2024) Parent APWeb-WAIM International Joint Conference on Web and Big Data Delivery In person Event Date 30 Aug 2024 to end of 01 Sep 2024 Event Location Jinhua, China |
Abstract | In recent years, the significant effect of knowledge sharing between similar traffic scenarios in traffic speed prediction has received widespread attention. Existing knowledge-sharing-based researchs usually capture spatial-temporal correlation of traffic flows directly through hard or soft sharing. However, such coarse-grained sharing is not sufficient to capture fine-grained local spatial-temporal dynamics. We argue that local fluctuations in traffic flow may be caused by traffic events, weather changes and others, and implicitly reflect some specific road network structure. To this end, we propose a fine-grained knowledge sharing framework that separates local fluctuations in traffic flow so that traffic prediction modelling can learn knowledge related to the road network structure, thereby improving prediction performance. Specifically, our framework consists of temporal and spatial modules to model traffic flow information. (1) Global changes of spatial-temporal dynamics are captured by the self-decomposition module of the spatial module which is directly shared between similar traffic scenarios. (2) Local fluctuations of spatial-temporal dynamics are captured by the graph convolution layer of the spatial module, and we add parameter constraints to the graph convolution parameters, aiming at shortening their parameter differences. In this way, the fine-grained knowledge sharing is achieved. Finally, skip connections are used to converge spatial-temporal correlations for final predictions. Experimental results on two city datasets and two highway datasets show that our proposed framework achieves state-of-the-art prediction performance in terms of Mean Average Error (MAE) and Root Mean Squared Error (RMSE). |
Keywords | Knowledge Share; Speed Prediction; Traffic Scenario |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460508. Information retrieval and web search |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland |
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