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Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences
| 题名： | Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences |
| 正文语种： | eng |
| 作者： | Shuai, Chunyan;Wang, WenCong;Xu, Geng;He, Min;Lee, Jaeyoung |
| 作者单位： | Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650041 Yunnan Peoples R China;Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650041 Yunnan Peoples R China;Kunming Urban Planning & Amp Design Inst Co Ltd Kunming 650041 Yunnan Peoples R China;Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China;Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650050 Yunnan Peoples R China |
| 关键词： | Expressway;Fully connected (FC);Long short-term memory (LSTM);Mean pooling layer;Short-term traffic flow forecast |
| 摘要： | Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 148 |
| 期： | 6 |
| 页码： | 04022026.1-04022026.9 |
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