Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events
Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/6833793 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850167248400941056 |
|---|---|
| author | Yichao Pu Xiangdong Xu Qianqi Fan Shengyu Zhang Jilai Chen |
| author_facet | Yichao Pu Xiangdong Xu Qianqi Fan Shengyu Zhang Jilai Chen |
| author_sort | Yichao Pu |
| collection | DOAJ |
| description | Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations. |
| format | Article |
| id | doaj-art-e2ebd8d4a14249bc999bde22ca91fa05 |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e2ebd8d4a14249bc999bde22ca91fa052025-08-20T02:21:14ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/6833793Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure EventsYichao Pu0Xiangdong Xu1Qianqi Fan2Shengyu Zhang3Jilai Chen4College of Electronic and Information EngineeringKey Laboratory of Road and Traffic Engineering of the Ministry of EducationKey Laboratory of Road and Traffic Engineering of the Ministry of EducationShanghai University of International Business and EconomicsFaculty of Mathematics and Physical SciencesAccurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations.http://dx.doi.org/10.1155/2024/6833793 |
| spellingShingle | Yichao Pu Xiangdong Xu Qianqi Fan Shengyu Zhang Jilai Chen Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events Journal of Advanced Transportation |
| title | Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events |
| title_full | Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events |
| title_fullStr | Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events |
| title_full_unstemmed | Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events |
| title_short | Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events |
| title_sort | short term interval prediction of inbound passenger flow of subway station under failure events |
| url | http://dx.doi.org/10.1155/2024/6833793 |
| work_keys_str_mv | AT yichaopu shorttermintervalpredictionofinboundpassengerflowofsubwaystationunderfailureevents AT xiangdongxu shorttermintervalpredictionofinboundpassengerflowofsubwaystationunderfailureevents AT qianqifan shorttermintervalpredictionofinboundpassengerflowofsubwaystationunderfailureevents AT shengyuzhang shorttermintervalpredictionofinboundpassengerflowofsubwaystationunderfailureevents AT jilaichen shorttermintervalpredictionofinboundpassengerflowofsubwaystationunderfailureevents |