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This paper presents an optimized logistic regression machine learning modelthat predicts the occupancy of an Electric Vehicle (EV) charging station giventhe occupancy of neighboring stations. The model was optimized for the time ofday. Trained on data from 57 EV charging stations around the University ofCalifornia San Diego campus, the model achieved an 88.43% average accuracy and92.23% maximum accuracy in predicting occupancy, outperforming a persistencemodel benchmark.

Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection