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• W3122415204 startingPage "20208" @default.
• W3122415204 abstract "In order to solve the problem of cross-regional customized bus (CB) route planning during the COVID-19, we develop a CB route planning method based on an improved Q-learning algorithm. First, we design a sub-regional route planning approach considering commuters' time windows of pick-up stops and drop-off stops. Second, for the CB route with the optimal social total travel cost, we improve the traditional Q-learning algorithm, including state-action pair, reward function and update rule of Q value table. Then, a setup method of CB stops is designed and the path impedance function is constructed to obtain the optimal operating path between each of the two stops. Finally, we take three CB lines in Beijing as examples for numerical experiment, the theoretical and numerical results show that (i) compared with the current situation, although the actual operating cost of optimized route increases slightly, it is covered by the reduction of travel cost of passengers and the transmission risk of COVID-19 has also dropped significantly; (ii) the improved Q-learning algorithm can solve the problem of data transmission lag effectively and reduce the social total travel cost obviously." @default.
• W3122415204 created "2021-02-01" @default.
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• W3122415204 date "2021-01-01" @default.
• W3122415204 modified "2023-10-16" @default.
• W3122415204 title "Cross-Regional Customized Bus Route Planning Considering Staggered Commuting During the COVID-19" @default.
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• W3122415204 doi "https://doi.org/10.1109/access.2021.3053351" @default.
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