Optimization Methods for Customized Bus Routes in Random Environments
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| Publicado en: | Journal of Advanced Transportation vol. 2025 (2025) |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
John Wiley & Sons, Inc.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1155/atr/1680317 |2 doi | |
| 035 | |a 3225275864 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 164028 |2 nlm | ||
| 100 | 1 | |a Gong, Fangyuan |u School of Traffic and Transportation Beijing Jiaotong University Beijing 100044 China | |
| 245 | 1 | |a Optimization Methods for Customized Bus Routes in Random Environments | |
| 260 | |b John Wiley & Sons, Inc. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The customized bus in operation faces numerous random factors that affect the service level and attractiveness to passengers. Therefore, this paper investigates the optimization problem of customized bus routes considering random vehicle travel times and the capability to respond to dynamic requests in real time. We developed a stochastic programming model that minimizes total cost and passenger travel time. The innovation lies in the model’s ability to respond to requests made by passengers during service and to model the randomness of vehicle travel times using a known distribution. Furthermore, we propose a heuristic algorithm combining the nondominated sorting genetic algorithm II (NSGA-II) and a variable neighborhood search operator. This algorithm starts by generating an optimized initial path based on initial reservation demands and then employs a dynamic adjustment mechanism to respond to real-time requests. The effectiveness and superiority of our algorithm are validated through an illustrative example. Finally, numerical experiments using taxi trajectory data demonstrate that considering both randomness and real-time aspects can significantly reduce the total cost and penalties for early and late arrivals and improve the bus service level. | |
| 653 | |a Randomness | ||
| 653 | |a Integer programming | ||
| 653 | |a Public transportation | ||
| 653 | |a Random variables | ||
| 653 | |a Algorithms | ||
| 653 | |a Buses | ||
| 653 | |a Passengers | ||
| 653 | |a Travel time | ||
| 653 | |a Stochastic models | ||
| 653 | |a Automobile customizing | ||
| 653 | |a Heuristic | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Traffic congestion | ||
| 653 | |a Customization | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Efficiency | ||
| 653 | |a Vehicles | ||
| 653 | |a Commuting | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Route optimization | ||
| 653 | |a Planning | ||
| 653 | |a Optimization | ||
| 653 | |a Quality of service | ||
| 653 | |a Literature reviews | ||
| 653 | |a Real time | ||
| 653 | |a Stochastic programming | ||
| 653 | |a Operating costs | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Jia, Chuanjun |u School of Traffic and Transportation Beijing Jiaotong University Beijing 100044 China | |
| 700 | 1 | |a Wu, Xu |u School of Traffic and Transportation Beijing Jiaotong University Beijing 100044 China | |
| 773 | 0 | |t Journal of Advanced Transportation |g vol. 2025 (2025) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3225275864/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3225275864/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3225275864/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |