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Novel Hybrid Spatiotemporal Convolution Neural Network Model for Short-Term Passenger Flow Prediction in a Large-Scale Metro System
| 题名： | Novel Hybrid Spatiotemporal Convolution Neural Network Model for Short-Term Passenger Flow Prediction in a Large-Scale Metro System |
| 正文语种： | eng |
| 作者： | Zhihong Li;Xiaoyu Wang;Hua Cai;Han Xu |
| 作者单位： | Dept. of Transportation Beijing Univ. of Civil Engineering and Architecture Beijing 102616 China||No. 15 YongYuan Rd. Daxing District Beijing 102616 China;Dept. of Transportation Beijing Univ. of Civil Engineering and Architecture Beijing 102616 |
| 关键词： | Passenger prediction; Spatiotemporal graph convolution; Bidirectional long short-term memory (BiLSTM); Attention mechanism; Subway networks |
| 摘要： | Accurate and reliable prediction of subway passenger flow is a particularly challenging application of spatiotemporal forecasting, due to the time-varying travel patterns and the complex spatial dependencies on subway networks. To address these challenges |
| 出版年： | 2024 |
| 期刊名称： | Journal of Transportation Engineering, Part A. Systems |
| 卷： | 150 |
| 期： | 5 |
| 页码： | 04024016.1-04024016.15 |
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