Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations
Abstract Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-06-01
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| Series: | Railway Engineering Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40534-023-00311-7 |
| _version_ | 1797637111364452352 |
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| author | Zishuai Pang Liwen Wang Shengjie Wang Li Li Qiyuan Peng |
| author_facet | Zishuai Pang Liwen Wang Shengjie Wang Li Li Qiyuan Peng |
| author_sort | Zishuai Pang |
| collection | DOAJ |
| description | Abstract Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences. |
| first_indexed | 2024-03-11T12:44:41Z |
| format | Article |
| id | doaj.art-66778713be23402e8ba6a539d493e273 |
| institution | Directory Open Access Journal |
| issn | 2662-4745 2662-4753 |
| language | English |
| last_indexed | 2024-03-11T12:44:41Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Railway Engineering Science |
| spelling | doaj.art-66778713be23402e8ba6a539d493e2732023-11-05T12:06:50ZengSpringerOpenRailway Engineering Science2662-47452662-47532023-06-0131435136910.1007/s40534-023-00311-7Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuationsZishuai Pang0Liwen Wang1Shengjie Wang2Li Li3Qiyuan Peng4School of Transportation and Logistics, Southwest Jiaotong UniversitySchool of Transportation and Logistics, Southwest Jiaotong UniversitySchool of Transportation and Logistics, Southwest Jiaotong UniversitySchool of Transportation and Logistics, Southwest Jiaotong UniversitySchool of Transportation and Logistics, Southwest Jiaotong UniversityAbstract Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences.https://doi.org/10.1007/s40534-023-00311-7Train operationsDwell timePassenger flowAveraging mechanismDynamic smoothing |
| spellingShingle | Zishuai Pang Liwen Wang Shengjie Wang Li Li Qiyuan Peng Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations Railway Engineering Science Train operations Dwell time Passenger flow Averaging mechanism Dynamic smoothing |
| title | Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations |
| title_full | Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations |
| title_fullStr | Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations |
| title_full_unstemmed | Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations |
| title_short | Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations |
| title_sort | dynamic train dwell time forecasting a hybrid approach to address the influence of passenger flow fluctuations |
| topic | Train operations Dwell time Passenger flow Averaging mechanism Dynamic smoothing |
| url | https://doi.org/10.1007/s40534-023-00311-7 |
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