Monetizing Parking IoT Data via Demand Prediction and Optimal Space Sharing

Abstract

Transportation is undergoing significant change due to advances in automotive technologies, such as electric and autonomous cars and transportation paradigms, such as car and ridesharing. Coupled with the rapid prevalence of IoT devices, this provides an opportunity for many organizations with large on-premise parking spaces, to better utilize this space, reduce energy footprint, and monetize data generated by IoT systems. This article outlines our efforts to instrument our University’s multistorey parking lot with IoT sensors to monitor real-time usage, and develop a novel dynamic space allocation framework that allows campus manager to redimension the car park to accommodate both car sharing and existing private car users. Our first contribution describes experiences and challenges in measuring car park usage on the university campus and removing noise in the collected data. Our second contribution analyzes data collected during 15 months and draws insights into usage patterns. Our third contribution employs machine learning algorithms to forecast future car park demand in terms of arrival and departure rates, with a mean absolute error of 4.58 cars per hour for a 5-day prediction horizon. Finally, our fourth contribution develops an optimal method for partitioning car park space that aids campus managers in generating revenue from shared cars with minimal impact on private car users.