A Charging Station Recommendation Method based on Link Prediction for Cruising Electric Taxis

The movement of a cruising ET (which do not pick up passengers) is quite sophisticated, and it depends on the driver’s subconscious movement tendency, driving habits, and historical pick-up experiences. This paper exploits the links between ETs and charging stations according to historical trajectories, and proposes a Charging station Recommendation Algorithm based on Link Prediction (CRA-LP) to recommend the optimal charging stations for cruising ETs. Specially, in CRA-LP, the Adamic-Adar index with a time decay effect is given to measure the similarities between ETs and charging stations. Simulations results show that CRA-LP achieves preferable results in terms of some metrics, such as Area Under Curve and Average Extra Movement, which also indicates that CRA-LP can predict the future links between cruising ETs and charging stations accurately, and then recommend the proper charging stations to cruising ETs to minimize the extra movements.