Smart Parking: Enhancing Urban Mobility with Fog Computing and Machine Learning-Based Parking Occupancy Prediction

Abstract

Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone.