WiParkFind: Finding Empty Parking Slots Using WiFi

With ever increasing number of vehicles, shortage of parking space is becoming a serious problem. Going to shopping, school, and workplace can be a headache as finding an available parking spot is getting harder causing wasted time and gas. In this paper, we present WiParkFind: a low-cost, non-intrusive, and real- time parking occupancy monitoring system based on WiFi signals. The channel state information (CSI) of received WiFi signals is analyzed by using a machine learning technique to capture distinctive characteristics of CSI data that are strongly correlated with the number of empty parking slots in order to detect whether there is an empty slot, and how many empty slots are available. Compared with contemporary approaches based on magnetic sensors deployed on individual parking slots, WiParkFind utilizes low-cost off- the-shelf WiFi devices, dramatically reducing the cost for purchasing, installing, and maintaining a large number of sensors, and backend server systems. A proof-of-concept system of WiParkFind was developed and deployed in a department parking lot. The results demonstrate that the average classification accuracy of WiParkFind over a week of data collection is 78.2%, and the accuracy is improved to 90.8% with a tolerance of one empty slot.