Hybrid genetic algorithm for an on-demand first mile transit system using electric vehicles
First/Last mile gaps are a significant hurdle in large scale adoption of public transit systems. Recently, demand responsive transit systems have emerged as a preferable solution to first/last mile problem. However, existing work requires significant computation time or advance bookings. Hence, we p...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/147728 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | First/Last mile gaps are a significant hurdle in large scale adoption of public transit systems. Recently, demand responsive transit systems have emerged as a preferable solution to first/last mile problem. However, existing work requires significant computation time or advance bookings. Hence, we propose a public transit system linking the neighborhoods to a rapid transit node using a fleet of demand responsive electric vehicles, which reacts to passenger demand in real-time. Initially, the system is modeled using an optimal mathematical formulation. Owing to the complexity of the model, we then propose a hybrid genetic algorithm that computes results in real-time with an average accuracy of 98%. Further, results show that the proposed system saves travel time up to 19% compared to the existing transit services. |
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