# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### Data Synthesis of Vehicle Detection Techniques (VDT) Implemented in SPS
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VDT** & **Number of Papers** & **Summary** & **References** \\ \hline IRS & 23 & \begin{tabular}{c} Monitors car parks via sensors \\ located on the pavement or road \\ surface. Sensors such as ultrasonic, \\ magnetic, and VLC are used. \\ \end{tabular} & \begin{tabular}{c} [19,73,75,81,91,94,100,103,109,127, \\ 129,156,168,179,184-186,211,225, \\ 226,232,239] \\ \end{tabular} \\ \hline ORS & 142 & \begin{tabular}{c} Implements sensors placed above \\ the surface of the road, i.e., working \\ over the vehicles and not \\ underneath them. These include \\ CCTV, IR sensors, and RFID circuits, \\ among others. \\ \end{tabular} & \begin{tabular}{c} [14,15,16,18,20-23,26-29,31-36,44-46, \\ 51,57-60,62,63,66,66,-70,76,77,79,82, \\ 84-88,90,93,94,101,105-108,111,113, \\ 115-117,119,121-123,126,128-130, \\ 155,157,159-165,167,170-178,180-185,189,191,195,200,205-208,213-215,217-221,223,224,226-238,242, \\ 263,266,269,271,273,274,287] \\ \end{tabular} \\ \hline RSU & 8 & \begin{tabular}{c} Implements V2R communications. \\ Data on the position of vehicles are \\ obtained by triangulating the RSU \\ units and are compared with the \\ location of the parking slots to \\ determine availability. \\ \end{tabular} & \begin{tabular}{c} [83,120,132,158,222,270,285,286] \\ \end{tabular} \\ \hline CS & 30 & \begin{tabular}{c} Detects available parking slots by \\ collecting data from citizens. Offers \\ advanced services through \\ intelligent data analysis that allows \\ useful information to be extracted. \\ This type of system depends entirely \\ on users participating. \\ \end{tabular} & \begin{tabular}{c} [38,39,40,41,47,72,104,125,135-138,140-151,169,202,290] \\ 151,169,202,290] \\ \end{tabular} \\ \hline PM & 10 & \begin{tabular}{c} Is based on prediction models with \\ historical data collected on the \\ availability of parking places. Some \\ propose a hybrid data collection, \\ together with information in \\ real time. \\ \end{tabular} & \begin{tabular}{c} [204,207,209,212,216,238,271,279, \\ 280,284] \\ \end{tabular} \\ \hline DFTP & 11 & \begin{tabular}{c} Detects parking slot availability by \\ obtaining data from a third party, \\ e.g., a government unit that controls \\ public parking, a communications \\ company that has its own detection \\ system, or another intelligent \\ parking system available. \\ \end{tabular} & \
\begin{tabular}{c} [24,30,74,112,114,124,183,210,272, \\ 288,289] \\ \end{tabular} \\ \hline WD & 50 &
\begin{tabular}{c} Describes proposals of SPS that \\ assume data on the availability of \\ parking slots will be obtained by \\ any means. In certain cases, an \\ average number of spaces is \\ assumed available, or the \\ information of availability is not \\ considered and centers only on the \\ algorithm or method implemented. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Number of publications for each VDT, along with a summary and references. Something worth mentioning is that all the articles that described neural network prediction (NNP) as a screening technique classified this as DS. There were no SPS that implemented an NNP, but these papers presented a description of this predictive technique for an SPS.