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Traffic State Prediction for Urban Networks: A Spatial-Temporal Transformer Network Model
| 题名： | Traffic State Prediction for Urban Networks: A Spatial-Temporal Transformer Network Model |
| 正文语种： | eng |
| 作者： | Xinkai Ji;Peipei Mao;Yu Han |
| 作者单位： | School of Transportation Southeast Univ. Nanjing 211100 China;School of Transportation Southeast Univ. Nanjing 211100 China;School of Transportation Southeast Univ. Nanjing 211100 China |
| 摘要： | Traffic state prediction plays an important role in traffic management; e.g.; it can provide travelers with accurate routing information to achieve a better travel experience. In this paper; we propose a spatial-temporal transformer network (STTN) model on the traffic state prediction for ubran networks. The STTN model integrates four modules: road embedding (RE); basic information embedding (BIE); temporal transformer (TT); and spatial-temporal transformer (STT). Specifically; the road topology information and other basic road information are embedded in the RE and BIE modules; respectively. The TT module; which is developed based on the Transformer encoder; captures the variation of the sequential historical traffic flow data. The STT module fuses a TT; which captures the spatial correlations and temporal dynamics of network traffic state; and the attention mechanism; which adjusts the importance of different historical data. The performance of the proposed STTN model is demonstrated using real traffic data collected from crowd-sourced vehicles. The proposed model achieves better prediction accuracy in terms of f1-score and weighted f1-score compared with those of other baseline models. The ablation study shows that some modules in the proposed STTN have a significant impact on improving short-term prediction ability. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 11 |
| 页码： | 04023105.1-04023105.14 |
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