Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. 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‐learning 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 years 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’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.
