IoT Enabled Smart Parking System for Improvising the Prediction Availability of the Parking Space

Abstract

Smart cities are a result of persistent technological advancements aimed at improving the quality of life for their residents. IoT-enabled smart parking is one of the foundations of smart transportation which seeks to be versatile, long-lasting, and integrated into a Smart City. One of the studies shows that the drivers who are searching for free parking space can cause congestion problems up to 30%. There is a possibility to reduce air pollution and fluidity noise traffic by combining Internet of Things (IoT) sensors positioned in different parking areas with a mobile application and help the drivers to search for free places in different areas of the city and also provide guidance toward the parking space. In this paper, we show and explain a unique Data Mining-based Ensemble technique for anticipating parking lot occupancy to reduce parking search time and improve car flow in congested locations, with a favorable overall impact on traffic in urban centers. In this paper multi scanning, IoT Enabled smart parking model is proposed along with ensemble classifier that improvises the predictive availability of the free parking space. The predictors' parameters were additionally optimized using a Bootstrap and bagging algorithm. The proposed method was tested an IoT dataset containing a number of sensor recordings. The tests conducted on the data set resulted in an average mean absolute error of 0.07% using the Bagging Regression method (BRM)