# Survey of Smart Parking Systems
## 6 Stage of Development
The second work analyzed is Reference [204]. Here, the data on the occupation of parking slots came from DFTP, in this case, from the city of Santander, Spain. Under the Smart Santander project [299], 400 sensors were located throughout the city, providing the necessary information about the state of parking space occupancy. This infrastructure involved retrieving parking sensor data every minute. With the data obtained from the city's sensors, an algorithm for parking occupancy prediction was proposed. For this purpose, the spaces were divided into four zones. Using a previously trained recurrent artificial neural network, the prediction of the occupation of the parking slots for the indicated zones was obtained. The user could also request the parking slot occupancy status. The data were retrieved and displayed on a digital site map or mobile phone. The results obtained showed that the proposal was able to accurately predict the region's parking occupancy rates for up to 30 min in the future. The third system implemented is Reference [21], in which they proposed a VDT with ORS implemented by RFID technology. An RFID reader counted the percentage of free parking slots in each car park. An RFID card that communicated with the reader identified each vehicle. The system, implemented in Taichung, Taiwan, provided a mechanism to avoid disputes over a parking slot and involved the process of reserving the parking slot to secure it for the user. However, it also maintained a parking alternative for vehicles that were not previously booked, indicating on the reservation form if there were spaces available. The proposed SPS was adapted to closed parking lots with entrance control where the RFID reader was located. The results showed that the algorithm reduced the average waiting time for users to park. The fourth work [223] proposed the use of WSN sensors for vehicle detection. The characteristics of the proposal were based only on the implementation of a VDT. Sensors were deployed in 50 nodes in the parking areas of the city of Riyadh, Saudi Arabia. It operated for three months. The results indicated that they obtained a 98% detection rate of vehicles in the parking slots. The fifth implementation [217] was based on a system implementing an ORS-type VDT. Its implementation consisted of computer vision logics and an IOT middleware solution. These technologies were used in the design of a network of visual sensors, which was physically deployed throughout the parking slots in Pisa, Italy for evaluation. Image analysis and frame differentiation were used to detect changes perform in a default region of interest (RoI) on the acquired frame. Each RoI corresponded to a specific parking slot. The visual assessment by means of cameras and computers that process the information allowed the status of the parking places to be obtained. The tests carried out on the public roads resulted in 95% of the information processed about each parking slot being correct. Finally, the last and sixth implementation is Reference [179]. VDT IRS geomagnetic sensors collected information from each parking slot and sent it to a server in the cloud. The server processed the data and fed two systems: a maintenance system, where it reported on the status of the sensors, and another for the users of the vehicles. A mobile application informed the drivers about the status of the parking slots in the city by obtaining data on where there were free places. However, it did not offer information about the route to the parking spot, nor any other indications to the user. Therefore,it can be classified as an SPS of the CAPS type. It was also integrated with a payment system so that users could pay for parking from their cell phones. The system was deployed in two cities in Zhejiang province, China. Table 14 shows the principal strengths and weaknesses of each implementation described previously.