Abstract
Traffic flow prediction, as one of the prominent tasks in intelligent transportation systems, is challenging due to underlying complex spatiotemporal characteristics. Consideration of historical spatial and temporal dependencies is essential for the traffic prediction of a geographic unit for a future time period. Existing works mainly adopted graphs to represent the irregular layout of spatial units, where nodes are signal of spatial units and edges are link strengths between units. For contemporary deep learning based spatiotemporal prediction tasks, the temporal dependence can be well modeled via convolution neural network or recurrent neural network, and spatial dependence features are commonly captured using graph convolution networks. However, classic graph structures cannot fully represent the complex nature of spatial relationships in transportation networks, because the spatial pattern of a location might be influenced by multiple sets of contextual information simultaneously, while a graph edge can only describe the linkage between two nodes. In addition, most existing models ignore the synchronous dependence between temporal and spatial features, leading to a mismatch between the temporal and spatial features of a location. Based on such problems, a hypergraph-based deep learning model, namely synchronous hypergraph convolutional network (SHGCN), is proposed to better capture the complex relationships between spatial and temporal knowledge. A novel synchronous hypergraph cell (SH-Cell) is designed based on LSTM cells integrated in the form of a Seq2seq architecture. Then, we construct dynamic hypergraphs to capture the synchronous spatiotemporal dependence adaptively using SH-Cells. Experimental results demonstrate the superiority of SHGCN over well-known benchmarks on two real-world publicly-available traffic datasets. This research provides new insights for improving the traffic flow prediction accuracy and understanding complex spatiotemporal relationships towards a more reliable urban traffic management.
Original language | English (US) |
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Title of host publication | Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2022 |
Editors | Bruno Martins, Dalton Lunga, Song Gao, Shawn Newsam, Lexie Yang, Xueqing Deng, Gengchen Mai |
Publisher | Association for Computing Machinery, Inc |
Pages | 30-39 |
Number of pages | 10 |
ISBN (Electronic) | 9781450395328 |
DOIs | |
State | Published - Nov 1 2022 |
Event | 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2022 - Seattle, United States Duration: Nov 1 2022 → … |
Publication series
Name | Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2022 |
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Conference
Conference | 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2022 |
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Country/Territory | United States |
City | Seattle |
Period | 11/1/22 → … |
Bibliographical note
Funding Information:This research is supported by the Faculty Set-up Funding of College of Liberal Arts, University of Minnesota (1000-10964-20042-5672018) and the Faculty Interactive Research Program from Center for Urban and Regional Affairs (1801-10964-21584-5672018).
Publisher Copyright:
© 2022 ACM.
Keywords
- hypergraph convolution network
- spatiotemporal prediction
- traffic flow prediction