The Prediction of Parking Space Availability

Abstract

Intelligent Parking Systems (IPS) allow customers to select a car park according to their preferences, rapidly park their vehicle without searching for the available parking space (place) or even book their place in advance avoiding queues. IPS provides the possibility to reduce the wastage of fuel (energy) while finding a parking place and consequently reduce harmful emissions. Some systems interact with in-vehicle navigation systems and provide users with information in real-time such as free places available at a given parking lot (car park), the location and parking fees. Few of these systems, however, provide information on the forecasted utilisation at specific time. This paper describes results of a traffic survey carried out at the parking lot of supermarket and the proposal of the model predicting real-time parking space availability based on these surveyed data. The proposed model is formulated as the non-homogenous Markov chains that are used as a tool for the forecasting of parking space availability. The transition matrices are calculated for different time periods, which allow for and include different drivers’ behaviour and expectations. The proposed forecasting model is adequate for potential use by IPS with the support of different communication means such as the internet, navigation systems (GPS, Galileo etc.) and personal communication services (mobile-phones).

IOT Based Smart Parking System Using Ensemble Learning

“…In the proposed model, the data is tested on various machine learning algorithms to test the accuracy of the prediction service. In [13], a non-homogeneous Markov chain model was used to predict the parking space availability. It is reasonable to state that the model accurately captures how the parking lot is used with the highest MAPE (mean absolute percentage error) of 17.3%, despite the fact that the model data is only based on the week's traffic survey.…”