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

Abstract

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 (R² = 0.9022) followed by the DT (R² = 0.8971), RR (R² = 0.8846), LaR (R² = 0.8632), and LiR (R² = 0.8613). Simulation results validate the prediction effectiveness of the problem model.