An IoT based parking recommendation system considering distance and parking lot flow

The IoT (Internet of Things) technology and applications to facilitate "smart city" is developing rapidly. In this paper, we propose a parking recommendation system that confirms with the real situation that can achieve a high successful probability to obtain a free parking space when arriving at the parking lot. In our model, we divide the parking process into two parts, and use queueing theory for estimation. The first part is to estimate the driving travel time consumption of reaching the recommended parking lot after the parking demander's sending the parking request. The time is used in the second part to predict the probability of successfully obtaining a free parking space after the path driving time elapsed. Thereby, the probability of failing to obtaining a free parking space after a long way driving can be reduced. We use the SUMO traffic simulator for verification. The analysis of experiment results show that the performance is satisfactory and efficient than other related or traditional decisionmaking methods.