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

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.INDEX TERMS Customized bus, route planning, reinforcement learning, Q-learning algorithm, time window.

Understanding the Complex Adaptive Characteristics of Cross-Regional Emergency Collaboration in China: A Stochastic Evolutionary Game Approach

“…The overall characteristics, operation mechanism, participants, and the implementation performance of cross-regional governance have been key issues in previous studies [16][17][18]. Further, scholars have carried out a series of studies focusing on the practice and exploration of cross-regional governance in different fields, such as environmental regulations [19], transportation [20], energy [21], public health [22], and social development. Among them, cross-regional emergency management is also at the core of cross-regional public affairs' management.…”

Benefiting signatories at others’ expense: Effects and spatial spillover of local emergency cooperation agreements in China

“…Cross‐regional cooperation not only exists to implement emergency reinforcement for the neighboring government but also to deal with the fluidity and extensiveness of crises (J. Liu et al, 2022). Simulation analyses showed that cross‐regional cooperation is conducive to resource mobilization and transportation, thus improving the effectiveness of crisis response (Lv et al, 2018; Qiu et al, 2021; Shao et al, 2018; A. Wang et al, 2021). In practice, cooperation frameworks and agreements are employed to instructed localities to better understand and respect each other's roles and responsibilities when formulating collaborative emergency management plans (Mcguire & Silvia, 2010).…”

Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

“…Zhou Jibiao, Zhang Haisu et al [1,2] Risks in the surrounding areas of COVID-19 were not considered Ma Chang-xi et al [3] Other modes of transportation are not considered Tu Qiang et al [4] Only the car network is considered Jia Fuqiang et al [5] The objective function is relatively simple Subramani et al [6] The risk avoidance path has a high probability of error Liping Fu et al [7] Models and algorithms to be expanded A. Khani et al [8] Risk factors in travel are not considered Xu Ke, Liu Sijia, Luo Fei et al [9][10][11] The algorithm and model need to be further improved Wang Keyin et al [12] The model is not suitable for urban traffic path planning Wang A. et al [13] Different preferences and requirements of passengers are not considered Levy S. et al [14] The setting of reward function needs further improvement Therefore, we use the SUMO simulator to build the actual road network model and design a method to extract the road network impedance matrix, which greatly improves the efficiency and accuracy of road network modeling. We have established an enhanced learning path planning model in the context of urban traffic, and designed a search mechanism to avoid risk related areas of the new epidemic.…”

“…Although the results are better than traditional methods, they still need some improvement in the work. A. Wang et al [13] proposed a cross-regional customized bus route planning algorithm based on improved Q-learning during the epidemic. Sharon Levy [14] of the United States introduced a new path generation method through deep reinforcement learning, which was able to successfully optimize the multi-criteria path selection problem.…”