1. Introduction

Traffic safety and the efficiency of traffic and passenger flow management are evaluated by the reliability and error tolerance of the software and technical means of the traffic management system. Therefore, it is important to use modern technologies of communication and management in the development of traffic management and traffic management systems. The development of such systems is a current demand and urgent issue.

Today, the organization of traffic in big cities, especially in the central parts of the city, is one of the big and pending issues. This problem can be explained by the fact that it has arisen due to the increase in the level of motorization. The partially or completely unorganized movement of vehicles on the roads not only affects traffic safety but also sharply reduces the capacity of the street network.

Partial or complete disruption of the movement of vehicles on the roads not only endangers traffic safety but also drastically reduces the capacity of the street network. The decision to change the construction of roads, that is, the construction of bridges, and tunnels with a sharp improvement in traffic in cities, is a long-term one, but it requires quite large financial and time costs. This problem can be solved without financial and time costs by using a computerized automatic traffic control system on the city road network.

Traffic safety, efficiency of traffic control and passenger flow is assessed by the reliability and fault tolerance of software and hardware means of the traffic control system. Therefore, it is important to use modern communication and control technologies when developing a traffic management and traffic management system. The development of such systems is an urgent need and an urgent task ^[1,2].

Traffic control algorithms based on traffic flow models are widely used in the transport network of cities. The requirements for the accuracy and complexity of the models are extremely high. Suffice it to say that at the simplest intersection, there can be 12 directions of movement of vehicles. For a section of the road network consisting of 10 such intersections, we are talking about 120 routes, and if the volume of traffic on each of these routes constantly changes in time and space, it is necessary to minimize delays.

Initially, the theory of traffic flows was an applied science that solved particular problems of transport (for example, the study and justification of the passability of roads and their intersections) and was based mainly on empirical data. In the early 1950s, the theory of traffic flows took shape and became an independent scientific direction. The ideas of statistics, hydrodynamics, etc. began to be used to describe traffic flows. This period is characterized by the rapid development of scientific thinking and the formation of the main directions of research ^[1].

M.Lighthill, G.B.Whitham, and P.Richards, in 1955 proposed the first macroscopic model and model (LWR-model), in which the traffic flow was considered from the point of view of continuum mechanics. Further development of the transport system of Russia, expansion of the road network and increase in connectivity, increasing the role of multimodal transportation, the introduction of intelligent transport systems, and the problem of congestion of the main transport corridors necessitated the use of transport models ^[3,4].

Prevention of traffic jams at traffic lights is one of the most pressing problems today. Every year the number of cars increases, which complicates the solution of this problem. As such traffic jams increase, the number of collisions and injuries to pedestrians also increase year by year, according to statistics. To ensure the safety and continuity of traffic around the world, traffic controllers use FLIR's intelligent technologies to improve traffic flows and transport systems on the roads.

In the field of traffic management, FLIR's detection and control solutions are successfully helping to manage traffic and ensure safety. FLIR software is also very useful for protecting the environment, detecting incidents on highways and tunnels, collecting traffic data, and public transport safety. The FLIR traffic sensor enables traffic cops to control traffic lights at intersections and efficiently manage city traffic. They also help optimize the safety of pedestrians, cyclists, and heavy traffic ^[3]. In addition to preventing accidents, thermal imaging allows the timely detection of fires in vehicles and helps dispatchers monitor the presence of passengers.

2. Methods of modelling transport flows

When designing any traffic interchange (intersections of city highways at one or different levels), the designer always has questions, for example, what kind of traffic mode the selected geometry creates at the designed intersection. Also, in other cases, each project for the reconstruction or construction of a new road junction requires approval in state programs.

However, when intersections of different levels and other complex nodes begin to appear in the road network of cities, our researchers set themselves the goal of obtaining reliable information about the structure of future road transport and the features of its operation. This is how the design stage or pre-project proposals (concepts) began to develop, as well as the section "Theory of traffic flows" and its mathematical model.

Our domestic scientists faced this problem much later than their foreign counterparts. In this regard, the models of the theory of traffic flows created by domestic researchers are not considered in this paper.

At the moment, foreign scientists are faced with the opposite situation. Donald Drew in his book "Theory of traffic flow and Control" analyzed the problem of realizing the problem faced by many young researchers who began work in the field of "Theory of traffic flow". In this case, the formulas are a collection of many types of models, ranging from a single formula to entire arrays that can only be processed by a computer. However, at the moment, the theory of traffic flow has long been a necessary tool in the field of traffic flow modelling, and there is no point in "reinventing the wheel" here. Only available resources should be properly used. This section discusses some well-known approaches to traffic flow modelling.

2.1 PTV Vision model in the Vissim software package

The Vissim is a simulation model for realistically simulating urban and intercity transportation, including pedestrian traffic, that is microscopic, time-stepping, and behavior-based. This simulation system consists of two separate programs that interact with each other through an interface where the measurement data of the detectors and information about the state of the control systems are exchanged.

Traffic flow is simulated based on various parameters, such as lane allocation, vehicle composition, signal management, and the detection of private and public vehicles. The result of the simulation is to animate traffic in graphical form in real time, and then display all kinds of traffic and technical parameters, such as the distribution of travel time and waiting time, differentiated by user groups (Fig 1).

Fig. 1. Main window of PTV Vissim software package

The traffic flow model includes a next-vehicle model and a lane change model to represent single-lane traffic in a column behind the vehicle ahead. Traffic control logic is modeled with external lighting control software ^[9]. The logic control program requests the parameters of the sensors in the period from 1 second to 1/10 of a second (depending on the installation and type of traffic light). Based on the obtained values and time intervals, the program determines the state of all control systems for the next simulation stage and includes them in the traffic flow simulation.

2.3 MOBIL model

Like other models, the MOBIL model addresses the issue of lane changes by considering the advantages (incentives) of driving as well as extra guidelines for road safety. The peculiarity of the lane change is that it depends on the decision-making behavior and the flow of the moving vehicle (Fig 2).

Fig. 2. Diagram of the interaction of vehicles when changing lanes

The criterion of the MOBIL model includes a direct consideration of the impact of nearby vehicles. Indicates the benefit (usefulness) of changing lanes directly to the driver. Determines the degree of influence of neighboring vehicles on the driver's decision to change lanes.

(1)

$ａ_{Ｃ}＋ P （\check{ａ}_{ｎ}－ａ_{ｎ}＋ａ_{ｎ}－\check{ａ_{ｎ}})＞\triangle ａ_{ｔｈ}$

Here $ａ_{ｃ}$ represents the new acceleration c of the car after the expected one, the $ｐ（ａ_{ｎ}＋ ａ_{０}－\check{ａ}_{０}）$ weighting factor p represents the total gain (or total loss for negative values) from lane change for adjacent vehicles. The threshold value on the right-hand side of the formula simulates some inertia in decision-making, and it prevents the line from changing.

We can clearly see traffic jams and traffic inconveniences in every city center of Samarkand. In this article, the intersection of Ibn Sina and Dahbed streets of the city of Samarkand was chosen as the object of study (Fig 3).

Fig. 3. Geographical location of the selected object

Table 1 The results of the study of movement along thestreets of Ibn Sina and Dahbed

The name of the street		The number of parking spaces for cars along the street	Number of parking lots by location			Carriage　way lanes
			45°-60°	90°	0°
Ibn Sino Street	Right	12	6	5	5	3
	Left	11	7		5	3
Dahbed street	Right	12	11		6	2
	Left	6	9		1	2

This area is an object that is often visited by the population of Samarkand. The population near the intersection of Ibn Sina and Dahbed streets is densely populated. Therefore, on the roads of Ibn Sina and Dahbed streets, especially during the beginning and end of classes. during the "rush hour" there are congestion and inconvenience for drivers and pedestrians, unfortunately, there are many traffic accidents.

Table 2 Traffic flow of the intersection Ibn Sino and Dahbed

Time	Week days
	Mon.	Tue.	Wed.	Thu.	Fri.	Sat.	Sun.
8:09	1758	1865	1854	1875	1885	1875	1100
9:10	2122	2100	2210	2142	2145	2089	1100
10:11	1850	1758	1897	1789	1689	1850	1152
11:12	1589	1576	1587	1489	1489	1468	1120
12:13	1895	1945	1977	1885	1884	1852	1122
13:14	1758	1688	1710	1565	1548	1573	1200
14:15	1885	1458	1876	1763	1695	1785	1130
15:16	1869	1755	1785	1689	1452	1489	1100
16:17	1899	1978	1888	1778	1852	1881	1103
17:18	1968	1982	1954	1895	1786	1785	1201
18:19	1789	1796	1775	1785	1897	1752	1113
19:20	1200	1125	1120	1129	1100	1052	850
20:21	487	428	389	387	345	385	203
21:22	221	224	241	224	210	205	127

In order to ensure traffic safety, all elements affecting the placement of cars in street conditions and safe traffic were analyzed. Today, car parking along the road has a major impact.

For this reason, with the help of the PTV VISSIM program, a study, and analysis were carried out on whether the parking capacity around objects of social and economic importance is at the required level and how much it affects the flow of cars parked from the side of the street adjacent to it. In order to improve the quality of service at the parking lot, it became necessary to increase the capacity of parking lots around urban socio-economic facilities.

Fig. 4. Graph of traffic flows at the Ibn Sina and Dahbed junction

To solve these problems, we can create a traffic model using the PTV Vissim program (Fig 4). To determine the current state of the object of study designated by us, a 1-hour video of the traffic flow was recorded during the daytime rush hour, and by analytically counting the number of cars that passed through the intersection, a computer model was created, and analyzed. In it, hourly values were calculated for one daytime and evening rush hour as of February 4, 2021 (Fig 5) ^[6].

Fig. 5. Computer model created in the PVT vissim program

In practice, speed or travel time is an important indicator of the quality of transport service in determining the amount of traffic flow and street capacity. At intersections, the main measure is not the speed of vehicles, but their delay. It determines the level of maintenance of city streets and is an important performance indicator.

When evaluating the quality of a controlled intersection, it is difficult to describe some performance indicators. There are several measures related to capacity analysis and modelling, all of which depend on many aspects of the experience of a pedestrian or driver crossing a signalized intersection. The most common metrics are average vehicle delay, average travel length, and number of vehicle stops.

These three metrics are the most commonly used performance metrics at signalized intersections because they are directly perceived by the driver. Many studied sources have shown that the throughput of intersections with traffic lights in cities ranges from 1400 to 1800 cars per hour ^[9]. The level of service (LOS) is also assessed when evaluating signalized intersections (Table 3).

Table 3 Level of Service (LOS)

LOS	Sec/аutо
A	≤ 10
B	> 10 – 20
C	> 20 – 35
D	> 35 – 55
E	> 55 – 80
F	> 80

Urban street conditions can vary greatly, and parking spaces, transit buses, road widths, surrounding connecting intersections, and other factors can significantly affect observed volumes ^[12]. Based on the results of a computer model created for specific conditions, it is possible to effectively manage traffic lights with different phases, check the correct organization of traffic and choose an alternative option that suits the conditions.

3. The results obtained

In this study, analyzing the results of the current situation, several theoretical methods of reducing the delay time and optimizing the time parameters of the traffic lights were considered to increase the throughput of the intersection. In the PTV Vissim program, the following results were achieved by optimizing the time parameters of traffic lights.Initially, the traffic light phases are optimized with the time parameters of the current traffic light state through the “Green Matrix” phase type of the PTV Vissim program (Fig 6).

Fig. 6. Entering phases into the “Intergreen Matrix”

Fig. 6. Entering phases into the “Intergreen Matrix”

Fig. 7. 2D view of Ibn Sino and Dakhbed streets of Samarkand in the PTV VISSIM program

Having received the indicators in the current state, we will receive the results from the program even in the absence of roadside parking spaces and compare them with the results obtained from the current state. Below is a diagram constructed in the program without accommodation (Fig 7), and the results obtained in the case without accommodation (표 4).

Analyzing the obtained results, we see that the road maintenance level in the current state is equal to LOS F, and without calculations, this indicator will improve to the LOS B level. When we get the practical results of 1 hour in the "rush hour" of this area, in the current state with street parking and no parking as a test, the average queue length, the number of cars, the average delay time, the program shows harmful gases, fuel consumption. The difference between such indicators was taken into account. For example, the difference in traffic, that is, the average length of the queue, was 128 meters in both cases.

One of the most urgent problems in cities today is transport. This indicator shows how traffic and parking problems are related to each other. To avoid traffic jams, it is necessary to use the capacity of the road as much as possible. According to the results obtained in the case of parking and no parking, the shoulder can carry 2080 more cars than under current conditions. A comparison of the average delay time in the two cases showed a difference of almost 15 minutes.

At the same time, as a result of standing vehicles on the road, the emission of harmful gases into the atmosphere has a great negative impact on the environment. As a result of studies conducted in this area, it was noticed that in this area 13808 COx (grams) of harmful gases were emitted in one hour under current conditions, and 3878 COx (grams) in non-residential conditions.　

Another important aspect of the transportation industry is fuel consumption. Of course, traffic jams occur due to placement, and as a result, fuel is wasted. According to the above results, 142 liters of fuel were used in the non-residential state compared to the residential state in the study area. To solve these problems, it would be advisable to turn residences in the area of Ibn Sina and Dahbed streets into multi-story parking lots operating in the "smart residence" system, provide students with the opportunity to use these residences, and also establish a fee for the use of residential premises.

Fig. 8. Time parameters on the phase section of a traffic light (optimized case)

After simulating the traffic situation with the PTV vissim software, the current throughput of the intersection increased from 4,328 to 4,368 vehicles per hour during peak hours. The maximum received queue length was reduced from 115 m to 80 m, and the average vehicle delay was reduced from 42.49 s/h to 22.79 s/h. Level of Service (LOS) increased from D to C as determined by program outcome.

4. Discussion

Analyzing the results we got in the PTV Vissim software, we can see that the throughput level, traffic jam length, and average delay time changed when we optimized the intersection in the single-phase section. Another convenience of the computer model is that we can visualize the split model (Fig 4~Fig 8).

Economic and environmental benefits of conducting this kind of research:

· save fuel that is consumed at the idle speed of the engine due to traffic jams at intersections

· a sharp decrease in the number of harmful substances that can be emitted into the atmosphere due to fuel economy under given conditions

· saving time spent by road users on the movement of the driver, passenger, or pedestrian

5. Conclusion

The connection between the problem of intersections with traffic and traffic, which has become the most urgent problem for developed and developing countries, has been studied. According to the above analysis, the fact that vehicles parked along the street have a significant impact on road safety requires research in this regard.

In the studies conducted by scientists in Uzbekistan, the use of intelligent transport systems in the design of the organization of transport problems and ways to eliminate them, as well as improving the efficiency of intersections and the effective organization of their work, are not given enough. It is necessary to study foreign experience and solve the issue of implementation on the example of the region on a scientific basis.

Currently, the results collected based on periodic studies are accepted as the main sources for planning activities for the organization of traffic and the construction of road structures in large cities. Old methods are used to determine speed, flow composition, and speed characteristics on highways.

For example, estimated traffic load data for the road networks in Tashkent and Samarkand were derived from information obtained from measurements taken at more than 50 reference points over a period of several days, up to a maximum of one month. It was proposed to create a complex of intelligent transport systems (ITS) to solve the above problems with a high degree of accuracy and to have a reliable database.