SlotFinder: A Spatio-temporal based Car Parking System

Abstract

Nowadays, the increasing number of vehicles and shortage of parking spaces have become an inescapable condition in big cities across the world. Car parking problem is not a new phenomenon, especially in a crowded city such as Dhaka, Bangladesh. Shortage of parking spaces leads to several problems such as road congestion, illegal parking on the streets, and fuel waste in searching for a free parking space. In order to overcome the parking problem, we develop a spatio-temporal based car parking system namely, SlotFinder. We collect the data of 408 buildings those have parking slots from seven different locations. We then cluster these data based on time and locations. Later, we train location wise vacant parking spaces by using stacked Long Short-Term Memory (LSTM) based on their temporal patterns. We also compare our technique with the baseline models and conduct an ablation analysis, which outperforms (lower RMSE and MAE of 0.29 and 0.24, respectively) than that of the previous approaches.