Assisting Motorists Using Parking Prediction through a Car App

Abstract

More persons depend on private cars, particularly when alternative transport such as public transport is not as efficient as required. The majority of motorists get caught in queues moving slowly through large cities. Parking becomes more of a challenge in areas where existing car parks provide limited parking spaces. The model for the study was created following an observational study. This required a drone taking top down images for building a dataset, which in turn was used to flag available parking slots consulting historic patterns. The dataset is currently available for research purposes. The vehicle-detection tool developed for this study was used to evaluate the manual logs of the dataset and obtained generally satisfactory results, albeit presenting some limitations. Different regression algorithms were tested on the dataset and the best one overall was selected for making predictions. After considering various techniques, a car app using web technologies and a Node.js framework was built. Through this solution, predictions made using the dataset have been stored in a MongoDB database, and passed on to a motorist through the app. A total of 18 motorists took part in a controlled experiment designed to enable the functional and usability testing of the app.