**2. Related Concepts and Works**

The massive development of the automotive industry causes complications not only in transport but also in the possibilities of vehicle parking. The situation is particularly critical in large cities and densely populated conurbations. Extensive solutions to this problem, i.e., by extending parking areas, have been exhausted in many cases. Therefore, an intensive approach to vehicle parking is currently being promoted, which is based on the establishment of appropriate rules for the efficient use of existing parking capacities in a given location. This method belongs to the wider issue of developed cities (Smart Cities). It is a highly topical subject concerning all cities in developed countries around the world.

There are a number of publications in world databases dealing with similar issues and using mathematical, GIS and other tools that inspired the authors to generate ideas for solving the given issue. The topics of improving the organization of transport and increasing the safety and fluidity of transport in the central parts of various cities were monitored. Another monitored topic was the respect of already implemented regulatory measures in transport related to the issue of illegally parked vehicles, reducing congestion, searching for free parking spaces and also the issue of reducing energy consumption and emissions. In terms of technical tools working with spatial location data, the use of GIS tools proved to be the most suitable.

The publications were selected according to the objective defined in the previous chapter, and according to criteria that specifies the wider aspects of the objective. The categories of topics are:


#### 5. Use of GIS technology to organize parking in the city.

For each of these categories, the methods used were evaluated and the possibilities of their use in our project were analyzed.

In [5], the authors provide a search character and deal with works aimed at optimizing traffic routes, parking problems and the detection and prevention of traffic accidents. The most widely used methods for the solution were machine learning (ML) and the Internet of Things (IoT). In paper [6], the problem of parking has been addressed by proposing an architecture to automate the parking process using the internet of things, artificial intelligence and multi agent systems. The authors in [7] offer a review of the literature that deals with a wider field of transport problems and its results are useful for solving general transport issues. The study in [8] deals with traffic management based on fog computing. The method is based on cloud computing and is used to control smart city traffic in real time. The paper in [9] presents a machine learning method for traffic management. These include parking monitoring, 5G communications and more. The study in [10] focuses on the design of new road signs for the use of wireless communication technologies. The principle of the proposed solution is the digitization of road signs and their display on the driver's desk of vehicles.

In our project, we used the principles of the machine learning method in the design of the control algorithm for the calculation of the optimized route of the monitoring vehicle and for the search for free parking places. The use of IoT is foreseen only in the C-ROADS project, when the vehicles will be equipped with the appropriate technology to participate in solving situations in an intelligent transport system.

A new parking model for cost and time optimization is described in [11]. Performance is measured by a special parameter. The work in [12] proposes a distributed system that informs drivers about free parking spaces in real time. The detection of vacant places works on the principles of computer vision and machine learning. The study in [13] proposes an intelligent, fully automated parking management system. It informs the driver about the number of free parking spaces in the immediate vicinity and saves fuel.

From these schematics, it was possible to use common computer vision algorithms to recognize images of the license plates of parked vehicles. Localization of a visitor's vehicle is realized by the GNSS method or by their location in the mobile operator's network using the on-board computer in the car or the driver's smartphone. Based on its current location, the web application or the smartphone application of the Brno parking system [14] searches for free parking places in the immediate vicinity, including the possibility of vehicle registration into the system. An evolutionary algorithm was used for a similar function in [15]. Another localization task based on the intelligent algorithm solved in [16] was to find the nearest place for charging electric vehicles. In our project, it was necessary to solve the integration of static and dynamic data from multiple sources. The use of data from static cameras in combination with IoT has been dealt with in [17–19].

The authors in [15] deal with a system for the allocation of free parking spaces—a system of available mathematical models that works on the basis of a genetic algorithm. The advantage of the system is the speed and efficiency of searching.

In [17], the authors present a system for data mining from independent sources, which are stored in city computers. Their system is designed to obtain more information for Smart Cities.

A new Smart City Network Design Tool is described in [18]. The system is based on human machine communication, monitors network traffic and optimizes network services.

In [19] there is an intelligent management system for managing a large number of IoT devices. The system is based on cloud computing and aims to optimize services within a Smart City.

Article [16] shows a case study for charging electric vehicles. The principle is an intelligent concept with the possibility of an ecosystem with a user interface for mobile applications. The project is part of the EU Horizon 2020.

One possible way of relieving traffic and reducing the number of vehicles is their sharing, as solved in [20]. In a study [21], the authors try to solve the problem of parking by introducing a bicycle sharing system where supply and demand are realized using smartphones. An important part of the actual parking space capacity monitoring system is the interoperability addressed in [22] by IoT. In terms of enforceability of offenses when parking vehicles, legal conditions are also important. The work in [23] addresses an analysis of the legal framework for smart city services including transport and parking.

The study in [20] discusses a car sharing system based on the use of cars in 10 different European cities. The knowledge can be used to design a system that allows car sharing based on prediction, usage and other parameters.

The work in [22] describes the Global IoT Services system, which enables interoperability within IoT between cities. The application has been verified in Smart Cities in Spain and South Korea.

The authors in [23] deal with IoT classification within a Smart City. It discusses four IoT cases: an intelligent parking system, intelligent street lighting, intelligent monitoring system and intelligent sensors. Each of these systems is analyzed in this work and their optimization is recommended.

The work in [21] discusses a bicycle sharing system in China. The system was introduced in 2008 and is based on empirical analysis. The result is the design of a new bicycle sharing system called Dockless bikeshare. This system has proved its worth and supports the Chinese Republic.

Most of the above work solves the problem of parking using modern methods from the field of information technology, web applications and multi-criteria decision making. Surprisingly, very few projects use GIS as an integrating element between static and dynamic spatial data. It is this principle that the authors chose as the basic element of the concept presented in the next chapter.

The authors in [24] use a web GIS in combination with multicriterial analysis (MCDA) to find parking spaces. The principle of the solution is group decision-making, which leads to a decrease in the share of information retrieval, average time spent gathering individual information and variability of information retrieval per attribute in the context of parking space selection.

The work in [25] uses GIS technology to select the optimal places for parking and charging electric cars in Germany. The system evaluates demand near points of interest.

The study in [26] presents the use of GIS to monitor traffic density and free parking spaces in Vilnius (Lithuania).

The issue of changing the standards of parking space dimensions in the context of the trend for purchasing and manufacturing larger vehicles that provide the best possible comfort is addressed in [27]. Parking space projects tend to design parking spaces for a representative set of vehicle types, but the effort is to minimize the dimensions of parking spaces. The article evaluates an analysis of vehicles from different countries and with different dimensions that are comparable to the dimensions of parking spaces.

### **3. The Concept of Transport Organization and Security System in the Historical Center of Brno**

The Residential Parking Project in Brno is part of the Transport Organization and Security System (SOBD) [4], which contains:


In 2018, the first results of the C-ROADS Czech Republic project [28], which is closely linked to the C-ROADS international initiative, which was the result of joint activities of the Czech Republic, Austria and Germany, were put into operation in Brno. The C-ROADS Czech Republic project is co-funded by the EU under the Connecting Europe Facility program (CEF). The main objective of the project was, in

cooperation with other European countries, to harmonize the provision of data communication services between vehicles and to allow communication between vehicles and intelligent transport infrastructure, thus creating an environment for the emergence of cooperative intelligent transport systems (C-ITS) including the possibility of using autonomous vehicles. The use of new technologies will contribute to greater safety for road users and smoother and more efficient transport, including achieving the effect of reducing emissions in the atmosphere. C-ITS systems inform drivers in a timely and accurate way about traffic conditions and warn of dangerous locations and other problems around them. In addition, traffic control and information centers receive accurate and comprehensive information on the current traffic situation directly from vehicles. As a result, it is possible to efficiently influence traffic flow and thereby increase traffic flow and safety and reduce its negative environmental impact [28]. The most important part of the traffic management in Brno is the Central Technical Control Center (CTD), which provides dispatching activity, remote supervision of traffic lights, evaluates information from the city surveillance system, monitors public transport preferences, operates a traffic information center [29], manages the parking system, home and paid parking in the city of Brno and cooperates with authorities, police and integrated rescue systems [30].

Citizens can also contribute to improving the quality of road maintenance through a mobile application to report defects on urban roads under the management of the Brno Communication Company [31]. In order to reduce the number of towing vehicles in the block cleaning of public roads, the application is in operation with their clear terms and locations [32].

Partial goals of optimization of the parking system:


Transport organization is generally a complex issue that cannot be solved without the support of new information technologies. Since it is a method that requires spatial information (position-related information in the reference coordinate system), it is possible to use GIS technologies.

There are four areas currently operating in the system. The central area—Brno center—which has specific rules and entrance to the historical city center is guided by the entry permissions. The other three resident parking areas are linked to the Central District in the northern part and are defined by specific streets. The resident parking system has been phased in. The first stage was launched in 2018. In March 2019, a new concept for residential parking in the city of Brno was approved. More information can be found in [4].

The principle of the proposed solution is based on the use of static (parking areas) and dynamic (monitoring data) spatial data processed in the geographical information system (GIS).

The parking monitoring system is based on the use of a mobile camera system for a large number of parking spaces located in the city of Brno. This technology is original in the Czech Republic. The mobile system is more efficient than the system of a large number of static cameras due to the limited possibilities for their installation, including the necessity of solving the problem of the necessary image quality.

The resident parking areas are shown in Figure 1, together with the types of traffic signs displayed. The red zone A (center of the city) is the entrance restricted zone (resident mode), the green zone B (central area of the city) is without entry restriction (subscription and visitor mode), blue zone C (edge zone) is not controlled during the day. The zones differ in the price of parking, the most expensive being the central zone. Examples of traffic signs illustrate how a car parking area in a given street zone is marked.

**Figure 1.** Residential parking areas map [14].

In Figure 1, areas with authorized parking are enclosed in red. Area 1-01 is the central area; areas 1-02, 1-13 and 1-14 are named after the area's most important street. Road signs in partial areas always include a supplementary table that identifies the area, e.g., 1-01, and specifies its restriction.

Resident parking space (Residents) it is intended for a natural resident in a demarcated area or for a property owner in a demarcated area.

Subscription parking place (Subscribers) it is intended for an entrepreneurial natural or legal person established in the demarcated area or for an entrepreneurial natural or legal person with an establishment in the demarcated area.

Visiting persons (Visitors) are neither subscribers nor residents. In the areas of the new parking system, which are marked with an orange band mark, short-term parking is free (60 or 30 min by price band, once every 24 h), then paid via parking meters or mobile app. Another solution is the use of parking houses or parking places. Persons going to visit a particular resident can ask for a temporary parking permit. Each resident (including children and non-residents) can split up to 150 parking hours a year.

Parking houses are buildings for toll parking in the center. Currently, there are eight parking houses with a capacity of 1310 parking spaces.

#### **4. Materials and Methods**

The parking system model is based on control data, which consists of three parts.

	- Parking area polygons;

Horizontal road markings are represented by polygons, and vertical markings are represented by points. In addition to the position, the data includes other attributes such as identification codes, types of stall, Zones of Parking Stages (ZPS) capacity, operating hours, etc.

	- Monitor triggers;
	- Links;
	- Routes and waypoints;
	- The street network.
	- Vehicle identifiers;
	- Driver identifiers;
	- Track identifiers.

Figure 2 is a diagram of the data flow between components of the parking system.

The basic pillar of control data is the existence of the polygons of pay stall areas and vertical traffic signs in vector form. The basis for creating polygons is a geodetic survey or project. The result is then a digital map with the polygons of parking areas within the paid parking zone. The Brno Communication Center uses GIS software from City Data Software, Ltd. (CDSw), in which it records the passport data of roads and their features in the city of Brno. One of the applications of this GIS software is Transport, where both horizontal and vertical traffic signs are recorded.

These polygons represent surfaces in individual layers:


**Figure 2.** Scheme of data flow between parking system components.

Each base layer polygon has its attribute table, which contains the object identification, time validity, city area, zone type, standing type (longitudinal, perpendicular, oblique), paid parking area designation, street network location, operating time according to the vertical road sign, parking spaces, editing history, etc. These polygons represent the entire parking space, which is usually a few parking spaces. Figure 3 shows an excerpt from a GIS Transport section designed by CDSw in the GISServer on the NexusDB platform.

There are sections of residential parking (fully green), forbidden areas, greenery, etc. (filler—brown grid), disabled places for a specific license plate (green grid), reserved parking places for doctors, restaurants, shops, etc. (purple hatch).

Figure 4 shows a view of the GIS Transport section with additional vertical traffic signs (traffic sign symbols) and a photograph of the parking meter and a vertical traffic sign indicating the parking area in the terrain that is part of the data stored in the database. There is also an example of a photograph of a border of a parking area in the terrain with blue lines. The vertical traffic sign is registered in the Transport application by a point element, with the cell displayed according to the created style in its actual form. An important item of traffic sign attributes is the validity that uniquely links the sign with the resident parking system. Sign attributes, like parking meters, contain a link to photo documentation of the sign in the field. Taking photo documentation, an essential part of the control data, is facilitated by the mobile application from CDSw company (San Diego, CA, USA), Praha, CR. This allows you to take photos of traffic signs through smart phones and assign them to the appropriate point in the Traffic app, so the operator will record the change and can respond to the change. If a new sign is taken, its photo can be assigned to a photo-point with coordinates found by the mobile, and the operator at the PC then assigns photo documentation to point attributes after creating the tag's data point.

**Figure 3.** Sections of residential parking in the Transport application.

**Figure 4.** Sections of resident parking with traffic signs.

An example showing how to locate the control data is shown in Figure 5. This illustration shows street polygons, control points, vertical traffic signs, and triggers. Paid parking areas are highlighted in pink.

**Figure 5.** Monitoring control data—sample from the Monitoring Data (MONDATA) database.

**Monitoring control data** includes so-called monitoring triggers, links between triggers and traffic signs, routes and points, including a street network. Monitoring control data is an important part of the entire system. Monitoring triggers are points with certain coordinates that trigger and stop monitoring when the monitoring car is in the same position. The trigger also contains parameters for a particular section of the pay-per-area section, for example, the type of parking. This allows the recognition process to estimate the angle of rotation of the registration number of the parked cars relative to the monitoring vehicle, thereby increasing the chance of it being correctly recognized from the camera image taken by the camera. The monitoring trigger also includes a preset azimuth under which the vehicle must be moved to trigger the monitoring. Triggers also stop monitoring at the end of the street to avoid recording vehicles outside the pay zone. Links between triggers and traffic signs give a more accurate pairing of road marking documentation and parked vehicle documentation. These are the lines between the trigger and the polygon, representing the parking space in the paid parking area or between the trigger and the vertical traffic sign.

The background data (signs and sections of paid parking) prepared in this way enter the GIS system SOBD, which uses two PostgreSQL™ databases. This is the SOBD database and the Monitoring Data (MONDATA) database. Data management is then performed through the open source geographic information system QGIS™. Data embedded in the SOBD database is synchronized with the MONDATA database data and vice versa, with set rules for editing. The MONDATA database is designed to create and edit triggers, links, waypoints, and street network lines. Some data, such as the attributes of monitoring cars, the drivers of these cars and routes, are without geometry. All this data is necessary and after connecting the monitoring car in the garage to the computer network, this data is inserted into the server in the car.
