Investigating the use of Machine Learning for Smart Parking Applications

Abstract

Traffic congestion caused by greater competition for limited parking spaces in the world’s major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine Learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.