Flexible Bus Route Optimization Scheduling Model
A flexible bus route optimization scheduling model that considers the dynamic changes of passenger demand is proposed to address the large difference in demand for flexible bus passengers and real-time variability. This model uses the heuristic algorithm based on gravity model to determine the follo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8816965 |
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| _version_ | 1832546870706044928 |
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| author | Ji-yang Sun Yan-yan Chen Jian-ling Huang Pan-yi Wei Cheng-cheng Song |
| author_facet | Ji-yang Sun Yan-yan Chen Jian-ling Huang Pan-yi Wei Cheng-cheng Song |
| author_sort | Ji-yang Sun |
| collection | DOAJ |
| description | A flexible bus route optimization scheduling model that considers the dynamic changes of passenger demand is proposed to address the large difference in demand for flexible bus passengers and real-time variability. This model uses the heuristic algorithm based on gravity model to determine the following: passenger booking; vehicle passenger capacity; team known conditions such as size, according to the dynamic changes of passenger demand for real-time iterative update shuttle travel time; vehicle operating costs (vehicle); and time cost for passengers (passengers waiting time for the vehicle, actual time of arrival, and the difference between expected and actual times of arrival) before minimization as the target. Finally, the practicabilities of the model and algorithm are verified by an example. Analysis results show that for 102 travel demands of 15 randomly generated demand points, completing all services requires 17–21 vehicles with average travel time of 24.59 minutes each. The solution time of 100 groups of data is within 25 seconds and the average calculation time is 12.04 seconds. Under the premise of real-time adjustment of connection planning time, this optimization model can thus better meet the dynamic demand of passengers compared with the current scenario. The model effectively reduces the planning path error, shortens the travel distance and passenger travel time, and achieves better results than the flexible bus scheduling model that ignores changes of connection travel time. |
| format | Article |
| id | doaj-art-4c2bffdb381848b5b21c1f644f833d4c |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4c2bffdb381848b5b21c1f644f833d4c2025-02-03T06:46:45ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/88169658816965Flexible Bus Route Optimization Scheduling ModelJi-yang Sun0Yan-yan Chen1Jian-ling Huang2Pan-yi Wei3Cheng-cheng Song4Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaCenter of Cooperative Innovation for Beijing Metropolitan Transportation, Beijing 100124, ChinaBeijing Transportation Information Center, Beijing 100161, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaA flexible bus route optimization scheduling model that considers the dynamic changes of passenger demand is proposed to address the large difference in demand for flexible bus passengers and real-time variability. This model uses the heuristic algorithm based on gravity model to determine the following: passenger booking; vehicle passenger capacity; team known conditions such as size, according to the dynamic changes of passenger demand for real-time iterative update shuttle travel time; vehicle operating costs (vehicle); and time cost for passengers (passengers waiting time for the vehicle, actual time of arrival, and the difference between expected and actual times of arrival) before minimization as the target. Finally, the practicabilities of the model and algorithm are verified by an example. Analysis results show that for 102 travel demands of 15 randomly generated demand points, completing all services requires 17–21 vehicles with average travel time of 24.59 minutes each. The solution time of 100 groups of data is within 25 seconds and the average calculation time is 12.04 seconds. Under the premise of real-time adjustment of connection planning time, this optimization model can thus better meet the dynamic demand of passengers compared with the current scenario. The model effectively reduces the planning path error, shortens the travel distance and passenger travel time, and achieves better results than the flexible bus scheduling model that ignores changes of connection travel time.http://dx.doi.org/10.1155/2021/8816965 |
| spellingShingle | Ji-yang Sun Yan-yan Chen Jian-ling Huang Pan-yi Wei Cheng-cheng Song Flexible Bus Route Optimization Scheduling Model Advances in Civil Engineering |
| title | Flexible Bus Route Optimization Scheduling Model |
| title_full | Flexible Bus Route Optimization Scheduling Model |
| title_fullStr | Flexible Bus Route Optimization Scheduling Model |
| title_full_unstemmed | Flexible Bus Route Optimization Scheduling Model |
| title_short | Flexible Bus Route Optimization Scheduling Model |
| title_sort | flexible bus route optimization scheduling model |
| url | http://dx.doi.org/10.1155/2021/8816965 |
| work_keys_str_mv | AT jiyangsun flexiblebusrouteoptimizationschedulingmodel AT yanyanchen flexiblebusrouteoptimizationschedulingmodel AT jianlinghuang flexiblebusrouteoptimizationschedulingmodel AT panyiwei flexiblebusrouteoptimizationschedulingmodel AT chengchengsong flexiblebusrouteoptimizationschedulingmodel |