Analysis and Prediction of Differential Parking Behaviors

Abstract

Nowadays, most modern parking lots integrate IoT technologies such as license plate recognition, mobile payment and automatic entrance/exit control to improve the convenience of the drivers and parking lot managers. However when searching for a vacant parking space becomes normally difficult in a metropolitan area, a more advanced Smart Parking System that can predict vacancy, enable reservation and differentiate pricing is even more needed. In this sense, understanding the parking behavior of customers has great significance to the manager of the parking estate, the drivers and the urban administrations. This paper explores the patterns and predictability of differential parking flows. With the license plate recognition and mobile payment system deployed in the parking lots, we collect about 14 million parking records of more than 300 parking lots in a big metropolitan in China. The time of every vehicle entering and exiting the parking lot is recorded along with its plate number. We first extract some features from these records and use K-means cluster algorithm to categorize the vehicle-parking lot pairs into three clusters empirically. The three clusters of parking behaviors are interpreted as regular parking users, long time visiting users and short time visiting users. Secondly, based on the parking lot's historical occupancy patterns, we designed several methods to predict the occupancy of the parking lot for different types of parking. The occupancies from the three types of vehicles can be used for making differential pricing policies or reservation policies.