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Accurate real-time energy consumption prediction of electric buses (EBs) is essential for bus operation and management, which can effectively mitigate the driving range anxiety while reducing the operation cost simultaneously. This paper presents a machine learning-based energy consumption prediction method for EB, which combines driving data with road characteristics data (such as road type), traffic condition (such as peak hour), and meteorology data (such as temperature). The importance of driving behavior features affecting energy consumption is quantitatively revealed by the novel Shapley additive explanation (SHAP). Given the road characteristics, traffic condition and meteorology information, a Long Short-Term Memory (LSTM) network is then used to predict driving microscopic parameters, including speed, acceleration, gas pedal position and brake pedal position. Finally, the instantaneous …