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The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self\u2010learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3\u2009years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers\u2019 waiting time and attract more passengers traveling by electric bus. 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