Affiliations: [a] School of Computer and Information, Anhui Normal University, Wuhu 241002, Anhui, China
| [b] Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China
Correspondence:
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Corresponding author. Chuanming Chen, School of Computer and Information, Anhui Normal University, No. 189 Jiuhua South Road, Wuhu, Anhui Province 241002, China. Tel.: +86 0553 5910645; E-mail: ccm1981@ahnu.edu.cn.
Abstract: With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time.
