Impact of Parking Fee on On-Street Night Parking Demand Prediction in Delhi using Machine Learning Approaches

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Parking during night hours is emerging in many residential areas, with limited spaces and high demand. This lack of off-street parking forces residents to rely on on-street parking, especially at night. To date, researchers have explored various machine learning (ML) techniques to predict daytime on-street parking needs. In this study, the authors investigated the on-street parking challenge during the night hours by focusing on a specific neighborhood in Delhi. The authors collected data on demographics, socioeconomic factors, and parking habits through surveys and used these to develop a nighttime parking demand model (with regard to the number of cars parked at night), which includes parking fees as a factor. In this article, to overcome the dilemma of on-street parking space for vehicles and highlight the most significant parameters, the authors have used five different ML approaches (linear regression [LiR], lasso regression [LaR], ridge regression [RR], decision tree [DT], and random forest [RF]). The best performing model was RF (R 2 = 0.9022) followed by the DT (R 2 = 0.8971), RR (R 2 = 0.8846), LaR (R 2 = 0.8632), and LiR (R 2 = 0.8613). Simulation results validate the prediction effectiveness of the problem model.