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- Publisher Website: 10.1109/TITS.2021.3129588
- WOS: WOS:000733537600001
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Article: NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms
Title | NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms |
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Authors | |
Issue Date | 2021 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2021, p. 1-12 How to Cite? |
Abstract | Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms. |
Persistent Identifier | http://hdl.handle.net/10722/309140 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LIANG, Y | - |
dc.contributor.author | Zhao, Z | - |
dc.date.accessioned | 2021-12-14T01:41:05Z | - |
dc.date.available | 2021-12-14T01:41:05Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2021, p. 1-12 | - |
dc.identifier.uri | http://hdl.handle.net/10722/309140 | - |
dc.description.abstract | Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.title | NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms | - |
dc.type | Article | - |
dc.identifier.email | Zhao, Z: zhanzhao@hku.hk | - |
dc.identifier.authority | Zhao, Z=rp02712 | - |
dc.identifier.doi | 10.1109/TITS.2021.3129588 | - |
dc.identifier.hkuros | 330871 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 12 | - |
dc.identifier.isi | WOS:000733537600001 | - |