# Survey of Smart Parking Systems
## 5 Algorithms or Methods Implemented in SPS
### Static Algorithms  
Static algorithms can be applied to route modeling where costs and restrictions do not vary over time [296]. These algorithms can establish the route from the vehicle to the parking slot, but do not modify the information about availability of the particular parking slot.  
This means that, once the shortest path is calculated, any change in the route or context requires a complete recalculation of the shortest path. Declared static routing algorithms are commonly used in modern vehicular navigation systems to quickly calculate routes between two points [297].  
A case of a static algorithm is the Dijkstra optimization algorithm, which is also used in other types of systems [162]. Other implementations complement a static algorithm, an ORS detection technique, and CAPS. They indicate to drivers the availability of a parking slot, but do not modify these visual notifications once the vehicle enters the car park [159].  
According to the research carried out, most of the work implemented this type of algorithm in PRS systems. This type of SPS includes a reservation module as the system's main assistance to minimize the time spent searching for parking slots. Through the reservation, car owners have a guaranteed parking space, which is why the route established to the space is static, since it ensures, in theory, its availability [15].  
Within this classification, genetic algorithms can be listed. These algorithms are efficient developments to find solutions to complex problems. They propose an optimization method based on populations, using the general principles of natural selection. In SPS, they are used to find the nearest free parking space, through operators such as crossover, mutation, and selection [73].  
This type of algorithm is executed as a search tool, using as parameters the preferences of the drivers. As these searches are complex and can involve many variables, they are executed in a previous stage to establish the route to the parking lot and generally do not have an update of the variables during its execution [256]; thus, they are grouped within the static algorithms.  
The selection process of these algorithms in that an SPS establishes parking populations represented by chromosomes. Each chromosome is made up of genes, and these are used as variables that describe characteristics of the problem for each car park. Such as the distance of the trip from the current position of the vehicle, the total time to reach the destination, and cost of the parking, among others. The set of these chromosomes is crossed (using the crossover operator) to obtain a new population of parking lots. Those that fit the objective function of the algorithm are selected and the rest are omitted. This procedure is repeated until the car park that best suits the user's preferences is reached [134].  
Other types of algorithms are artificial neural networks (ANN), which are machine learning algorithms that mimic the behavior of biological neural networks.  
In SPS, the use of neural networks was introduced to predict time series of parking occupancy in different regions of a city or to predict the probability of a parking space becoming free in the following time intervals. They are very robust and accurate algorithms with a high hit rate.  
As they are prediction tools, they are grouped within the static algorithms, since their execution is established in the phases prior to the execution of the route to a given car park.