The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.
1. Introduction
The vehicle routing problem (VRP) is one of the most widely studied problems in intelligent transport systems (ITS) today. The growth of cities in terms of population and number of vehicles led to a search for alternatives for a better transport system. The considerable number of vehicles trying to access areas that remain permanently congested means that finding a public parking space is often difficult, creating even more traffic chaos, as well as greater fuel consumption and, thus, higher greenhouse gas emissions. The routing techniques that comprise VRP study the search for routes for a vehicle wanting to move from an origin point to a destination point. They can comprise different selection criteria for identifying and selecting the optimal path [1,2,3].
The vehicle routing problem (VRP) is one of the most widely studied problems in intelligent transport systems (ITS) today. The growth of cities in terms of population and number of vehicles led to a search for alternatives for a better transport system. The considerable number of vehicles trying to access areas that remain permanently congested means that finding a public parking space is often difficult, creating even more traffic chaos, as well as greater fuel consumption and, thus, higher greenhouse gas emissions. The routing techniques that comprise VRP study the search for routes for a vehicle wanting to move from an origin point to a destination point. They can comprise different selection criteria for identifying and selecting the optimal path [1][2][3].
Smart parking systems (SPS) use a variety of technologies and advanced research. These systems are implemented in many environments and have a variety of features, which solve the problems faced by vehicle drivers in their day-to-day search for parking spaces. SPS takes advantage of a variety of complementary technologies such as sensors and routing algorithms [4]. SPS and VRP are closely associated because the search for a parking spot involves establishing a route, which must be optimum according to the context in which the system is operating. Functions to determine costs in the path may be complex and may involve various pieces of information which can change dynamically, for example, the driver’s preferences of which streets to take, several destination points, the places for parking that are occupied while driving, the time required to get to that place, information about where the traffic lights are, information about roads that are congested or blocked, and so on. In addition, the destination can vary if the parking spot the driver is supposed to head for becomes busy while they are on their way to it which, in turn, implies that the destination point will change. All these data mean constant re-planning and updating the route from the current location of the vehicle to the desired destination is required. The algorithms can provide results that may not prove to be entirely ideal or executing them can be costly in terms of the time required for the procedure. Thus, the routing algorithm not only has to be quick enough to find the optimal path to a vacant parking place according to the needs of the driver, but also has to be adaptable to the changing environment.
Smart parking systems (SPS) use a variety of technologies and advanced research. These systems are implemented in many environments and have a variety of features, which solve the problems faced by vehicle drivers in their day-to-day search for parking spaces. SPS takes advantage of a variety of complementary technologies such as sensors and routing algorithms [4]. SPS and VRP are closely associated because the search for a parking spot involves establishing a route, which must be optimum according to the context in which the system is operating. Functions to determine costs in the path may be complex and may involve various pieces of information which can change dynamically, for example, the driver’s preferences of which streets to take, several destination points, the places for parking that are occupied while driving, the time required to get to that place, information about where the traffic lights are, information about roads that are congested or blocked, and so on. In addition, the destination can vary if the parking spot the driver is supposed to head for becomes busy while they are on their way to it which, in turn, implies that the destination point will change. All these data mean constant re-planning and updating the route from the current location of the vehicle to the desired destination is required. The algorithms can provide results that may not prove to be entirely ideal or executing them can be costly in terms of the time required for the procedure. Thus, the routing algorithm not only has to be quick enough to find the optimal path to a vacant parking place according to the needs of the driver, but also has to be adaptable to the changing environment.
The current state of the art of SPS, presented in Reference [4], reviewed SPS, providing a brief description of this type of technology and classifying the systems into five main categories: parking guidance and information system, transit based information system, smart payment system, e-parking, and automated parking. The study also looks at the techniques for detecting whether a parking space is occupied or not, and how this is related to the systems previously classified. A similar article is a survey [5] that focused on exploring the concept of SPS and their categories. The survey classified the various existing parking systems and their corresponding technologies and categorized them into a summary form. Consequently, the following classification was proposed: centralized assisted parking search, non-assisted parking search, opportunistically assisted parking search, parking guidance and information system, transit based information system, smart payment system, automated parking, e-parking, car park occupancy information system, parking reservation system, intelligent transport system, intelligent parking assist system, and agent-based guiding system. Polycarpou and other authors [6] presented the results of a survey concerning drivers’ parking infrastructure needs from the perspective of intelligent services. They concluded that SPS is currently becoming an important need in urban areas. In addition, they addressed the latest trends in monitoring the availability of parking in parking reservation and dynamic pricing schemes.
2. Current state
Another review [7] focused on describing vehicle monitoring and vehicle detection technique (VDT) using sensors and other technologies in conjunction with sending this information to drivers via Short Message Service (SMS) or user interfaces. Kotb, Shen, and Huang [8] presented a review that described two types of SPS: parking guidance and information systems and parking reservation systems; they also developed the description of the VDT for these SPS. Other related work [9] presented a limited survey on SPS that focused the work on the concept of this area of research and developed a comparison between already published work that implemented solutions for these systems, along with a brief description of the technologies and services used by each of them. The limitations we encountered were due to the small number of papers analyzed between 2013 and 2015, as this did not allow us to draw conclusions from a broad spectrum of literature. Finally, in Reference [10], the authors introduced a smart parking ecosystem overview and proposed the following classification by functionality and problem focuses: information collection, system deployment, and service dissemination. In addition, the authors presented a classification of research projects, patents, commercial solutions, and municipal deployments.
The current state of the art of SPS, presented in Reference [4], reviewed SPS, providing a brief description of this type of technology and classifying the systems into five main categories: parking guidance and information system, transit based information system, smart payment system, e-parking, and automated parking. The study also looks at the techniques for detecting whether a parking space is occupied or not, and how this is related to the systems previously classified. A similar article is a survey [5] that focused on exploring the concept of SPS and their categories. The survey classified the various existing parking systems and their corresponding technologies and categorized them into a summary form. Consequently, the following classification was proposed: centralized assisted parking search, non-assisted parking search, opportunistically assisted parking search, parking guidance and information system, transit based information system, smart payment system, automated parking, e-parking, car park occupancy information system, parking reservation system, intelligent transport system, intelligent parking assist system, and agent-based guiding system. Polycarpou and other authors [6] presented the results of a survey concerning drivers’ parking infrastructure needs from the perspective of intelligent services. They concluded that SPS is currently becoming an important need in urban areas. In addition, they addressed the latest trends in monitoring the availability of parking in parking reservation and dynamic pricing schemes.
All these studies presented different and interesting results with respect to the solutions for SPS, as well as VDT. However, they lacked an analysis of how these systems relate to the described techniques, in addition to not having an exhaustive study of the types of algorithms that are implemented in these systems, which would allow for their characteristics to be grouped and classified. Finally, we did not find a classification that allowed us to determine the stages of development the studies analyzed are at. We wanted to do this to establish which SPS are implemented, simulated, or just proposed.
Another review [7] focused on describing vehicle monitoring and vehicle detection technique (VDT) using sensors and other technologies in conjunction with sending this information to drivers via Short Message Service (SMS) or user interfaces. Kotb, Shen, and Huang [8] presented a review that described two types of SPS: parking guidance and information systems and parking reservation systems; they also developed the description of the VDT for these SPS. Other related work [9] presented a limited survey on SPS that focused the work on the concept of this area of research and developed a comparison between already published work that implemented solutions for these systems, along with a brief description of the technologies and services used by each of them. The limitations we encountered were due to the small number of papers analyzed between 2013 and 2015, as this did not allow us to draw conclusions from a broad spectrum of literature. Finally, in Reference [10], the authors introduced a smart parking ecosystem overview and proposed the following classification by functionality and problem focuses: information collection, system deployment, and service dissemination. In addition, the authors presented a classification of research projects, patents, commercial solutions, and municipal deployments.
All these studies presented different and interesting results with respect to the solutions for SPS, as well as VDT. However, they lacked an analysis of how these systems relate to the described techniques, in addition to not having an exhaustive study of the types of algorithms that are implemented in these systems, which would allow for their characteristics to be grouped and classified. Finally, we did not find a classification that allowed us to determine the stages of development the studies analyzed are at. We wanted to do this to establish which SPS are implemented, simulated, or just proposed.
This entry uses a survey to determine what types of SPS are reported, to classify them, describe the VDT, algorithms, and methodologies used by them, as well as the relationship between these classifications, and finally indicate their stage of development. Furthermore, we analyze the status of this research area and obtain more information on the most commonly used methods and systems; then, based on the results attained, we determine what challenges SPS are currently facing.
This paper uses a survey to determine what types of SPS are reported, to classify them, describe the VDT, algorithms, and methodologies used by them, as well as the relationship between these classifications, and finally indicate their stage of development. Furthermore, we analyze the status of this research area and obtain more information on the most commonly used methods and systems; then, based on the results attained, we determine what challenges SPS are currently facing. The work is organized as follows: Section 2 defines the methodology used to search for the primary papers in this work, while Section 3 presents the types of SPS classified from the 274 studies selected. Section 4 explains the VDT implemented in the SPS detailed in Section 3, while Section 5 details the algorithms and methods obtained. Section 6 shows the stages of development of all the work studied, while Section 7 discusses the analysis carried out during the research and explains the gaps and open issues identified by our review. Finally, Section 8 draws some conclusions.
Encyclopedia
