Cross-Regional Customized Bus Route Planning Considering Staggered Commuting During the COVID-19

Ange Wang; Hongzhi Guan; Pengfei Wang; Liqun Peng; Yunqiang Xue

doi:10.1109/ACCESS.2021.3053351

Cross-Regional Customized Bus Route Planning Considering Staggered Commuting During the COVID-19

IEEE Access. 2021 Jan 21:9:20208-20222. doi: 10.1109/ACCESS.2021.3053351. eCollection 2021.

Authors

Ange Wang¹, Hongzhi Guan¹, Pengfei Wang^{2

3}, Liqun Peng⁴, Yunqiang Xue⁴

Affiliations

¹ Faculty of Urban ConstructionBeijing University of Technology Beijing 100124 China.
² Key Laboratory of Urban Security and Disaster Engineering of Ministry of EducationBeijing University of Technology Beijing 100124 China.
³ College of Urban ConstructionHebei Normal University of Science and Technology Qinhuangdao 066004 China.
⁴ School of Transportation and LogisticsEast China Jiaotong University Nanchang 330013 China.

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.

Keywords: Customized bus; Q-learning algorithm; reinforcement learning; route planning; time window.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant 71971005, in part by the Project Sponsored by Beijing Municipal Natural Science Foundation under Grant 8202003, in part by the Natural Science Foundation of Hebei Province under Grant E2018407051, in part by the Hebei Province for the Returned Overseas Chinese Scholars under Grant C20190333, and in part by the Fundamental Research Funds for Universities of Hebei Province under Grant 2020JK020.