A Data-Driven Approach to Understanding and Predicting the Spatiotemporal Availability of Street Parking

Abstract

Searching for a parking spot in metropolitan areas is a great challenge comparable to the Hunger Games, especially in highly populated areas such as downtown districts and job centers. Onstreet parking is often a cost-effective choice compared to parking facilities such as garages and parking lots. However, limited space and complex parking regulation rules make the search process of onstreet parking very difficult. To this end, we propose a data-driven framework for understanding and predicting the spatiotemporal availability of on-street parking using the NYC parking tickets open data, points of interest (POI) data and human mobility data. Four popular types of spatial analysis units (i.e., point, street, census tract, and grid) are used to examine the effects of spatial scale in machine learning predictive models. The results show that random forest works the best with the highest accuracy scores for the spatiotemporal availability classification across all four spatial analysis scales.