Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Abstract

Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics.