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

Zhijian Wang (), Jianpeng Yang, Qiang Zhang and Li Wang
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Zhijian Wang: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Jianpeng Yang: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Qiang Zhang: Beijing Aerospace Measurement & Control Technology Co. Ltd., Beijing 100041, China
Li Wang: Beijing Aerospace Measurement & Control Technology Co. Ltd., Beijing 100041, China

Sustainability, 2022, vol. 14, issue 20, 1-25

Abstract: The outbreak of COVID-19 brought great inconvenience to people’s daily travel. In order to provide people with a path planning scheme that takes into account both safety and travel distance, a risk aversion path planning model in urban traffic scenarios was established through reinforcement learning. We have designed a state and action space of agents in a “point-to-point” way. Moreover, we have extracted the road network model and impedance matrix through SUMO simulation, and have designed a Restricted Reinforcement Learning-Artificial Potential Field (RRL-APF) algorithm, which can optimize the Q-table initialization operation before the agent learning and the action selection strategy during learning. The greedy coefficient is dynamically adjusted through the improved greedy strategy. Finally, according to different scenarios, our algorithm is verified by the road network model and epidemic historical data in the surrounding areas of Xinfadi, Beijing, China, and comparisons are made with common Q-Learning, the Sarsa algorithm and the artificial potential field-based reinforcement learning (RLAFP) algorithm. The results indicate that our algorithm improves convergence speed by 35% on average and the travel distance is reduced by 4.3% on average, while avoiding risk areas, compared with the other three algorithms.

Keywords: urban traffic; path generation; reinforcement learning; resident travel; COVID-19; restricted search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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