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Network-Scale Passenger Flow Forecasting Methods in URT Based on Similarity Measurement
| 题名： | Network-Scale Passenger Flow Forecasting Methods in URT Based on Similarity Measurement |
| 正文语种： | eng |
| 作者： | Bao Wang;Xia Luo;Zongwei Wang;Qiming Su |
| 作者单位： | School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu 611756 China;School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong Univ. Chengdu 611756 China;School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu 61 1756 China;School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu 611756 China |
| 关键词： | Urban rail transit (URT); Passenger flow prediction; Similarity graph; Deep learning; Long short-term memory (LSTM) |
| 摘要： | Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators' timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 2 |
| 页码： | 04022141.1-04022141.12 |
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