Automated traffic control systems. Traffic Control Systems Basic Concepts of Traffic Control

In general, management means influencing a particular object in order to improve its functioning. In road traffic, the object of control is transport and pedestrian flows. Road traffic is a specific control object, since car drivers and pedestrians have their own will and realize their personal goals while driving. Thus, road traffic is a technosocial system, which determines its specificity as an object of management.

The essence of control is to oblige drivers and pedestrians, prohibit or recommend them certain actions in the interests of ensuring speed and safety. It is carried out by including the relevant requirements of the Traffic Rules, as well as by using a set of technical means and administrative actions of traffic police inspectors and other persons who have the appropriate authority.

At the level of traffic services, traffic management is a set of engineering and organizational measures on the existing road network that ensures safety and sufficient speed of transport and pedestrian flows. Such measures include management traffic, which, as a rule, solves narrower issues. Separate view control is regulation, that is, maintaining movement parameters within specified limits.

There are automatic, automated and manual traffic control systems. Automatic control is carried out without human participation according to a predetermined program, automated control is carried out with the participation of a human operator. The operator, using a set of technical means to collect the necessary information and search optimal solution, can adjust the operating program of automatic equipment. In both the first and second cases, computers can be used in the control process. The automatic control loop can be either closed or open. And finally, there is manual control, when the operator, assessing the traffic situation visually, influences the traffic flow based on existing experience and intuition.

In a closed loop, there is feedback between the means and the control object (traffic flow). Automatic feedback can be provided by special information collection equipment - vehicle detectors. The information is entered into the automation equipment and, based on the results of its processing, these devices determine the operating mode of traffic lights or road signs that can change their meaning upon command (controlled signs). This process is called flexible or adaptive management.

When the loop is open, when there is no feedback, road controllers (DCs) that control traffic lights switch signals according to a predetermined program. In this case, constant program control is carried out.

At manual control feedback always exists due to the operator's visual assessment of traffic conditions.

In accordance with the degree of centralization, two types of management can be considered: local and systemic. Both types are implemented using the above methods. With local control, signal switching is provided by a controller located directly at the intersection. In a system-based system, intersection controllers, as a rule, perform the functions of translators of commands arriving via special communication channels from a control point (CP). When controllers are temporarily disconnected from the control panel, they can provide local control.

In practice, the terms “local controllers” and “system controllers” are used. The former have no connection with the control panel and work independently, the latter have such a connection and are able to implement local and system control.

The equipment located outside the control center was called peripheral (traffic lights, controllers, vehicle detectors), and the equipment at the control center was called central (computer equipment, control systems, telemechanics equipment, etc.).

With system control, the system operator is located in the control center, that is, far from the control object, and to provide him with information about traffic conditions, communication means and special information display tools can be used (Fig. 8.1).

Figure 8.1 - General form control center

The latter are made in the form of luminous maps of the city or areas - mnemonic diagrams, which have equipment for visual display of graphic and alphanumeric information using a computer on displays and television systems, allowing direct observation of the controlled area.

Local control is most often used at a separate or, as they say, isolated intersection, which has no connection with neighboring intersections either for control or for flow. Changes in traffic light signals at such an intersection are provided according to an individual program, regardless of traffic conditions at neighboring intersections, and the arrival of vehicles at this intersection is random.

The organization of coordinated changes in signals at a group of intersections, carried out in order to reduce the time it takes for vehicles to move in a given area, is called coordinated control (control according to the “green wave” principle). In this case, as a rule, system coordinated control is used.

“The organization of traffic at the level of traffic services represents a complex of engineering and organizational measures on the existing road network, ensuring safety and sufficient speed of transport and pedestrian flows. Such activities include traffic management, which, being integral part traffic organization, as a rule, solves narrower problems. In general, management means influencing a particular object in order to improve its functioning. In relation to road traffic, the object of control is transport and pedestrian flows. A particular type of traffic control is regulation (from Latin word regulare - to subordinate to a certain order, rule, arrange), i.e. maintaining movement parameters within specified limits.
Taking into account the fact that regulation is only a special case of both control and traffic organization, and the purpose of using technical means is to implement its scheme, the textbook uses the term technical means of traffic organization or technical means of traffic control. This corresponds to the currently accepted terminology recorded in regulatory documents and the name academic discipline“Organization of traffic”, the logical continuation of which is the materials presented in this textbook.
At the same time, the term regulation, due to established tradition, has become widespread. For example, in the Traffic Rules, intersections and pedestrian crossings equipped with traffic lights are called regulated, in contrast to unregulated ones, where there are no traffic lights. There are also terms regulation cycle, regulated direction, etc. In the specialized literature, an intersection equipped with traffic lights is called a traffic light object. Taking this circumstance into account, in the textbook, in relation to each specific case, the terms that are most widely used and therefore most understandable to the reader are used.
The essence of traffic control is to oblige drivers and pedestrians, prohibit or recommend them certain actions in the interests of ensuring speed and safety. It is carried out by including the relevant requirements in the Traffic Rules, as well as by using a set of technical means and administrative actions of road patrol inspectors and other persons with appropriate authority.
The control object, a set of technical means and teams of people involved in the technological process of motion control form a control loop. Since some of the functions in the control loop are often performed by automatic equipment, the terms automatic control or control systems have developed.
Automatic control is carried out without human participation according to a predetermined program, automated control is carried out with the participation of a human operator. The operator, using a set of technical means to collect the necessary information and find the optimal solution, can adjust the operating program of automatic devices. In both the first and second cases, computers can be used in the control process. And finally, there is manual control, when the operator, assessing the transport situation visually, exerts a control action based on existing experience and intuition. The automatic control loop can be either closed or open.
In a closed loop, there is feedback between the means and the control object (traffic flow). It can be carried out automatically by special information collection devices - vehicle detectors. The information is entered into automation devices, and based on the results of its processing, these devices determine the operating mode of traffic lights or road signs that can change their meaning upon command (controlled signs). This process is called flexible or adaptive management.
When the loop is open, when there is no feedback, the devices that control traffic lights - road controllers (DCs) switch signals according to a predetermined program. In this case, strict software control is carried out.
In accordance with the degree of centralization, two types of management can be considered: local and systemic. Both types are implemented using the methods described above.
With local control, signal switching is provided by a controller located directly at the intersection. In a system-based system, intersection controllers, as a rule, perform the functions of translators of commands arriving via special communication channels from a control point (CP). When controllers are temporarily disconnected from the UE, they can also provide local control. The equipment located outside the control point is called peripheral (traffic lights, controllers, vehicle detectors), while at the control point it is called central (computer equipment, dispatch control, telemechanics devices, etc.).
In practice, the terms local controllers and system controllers are used. The former have no connection with the UE and work independently, the latter have such a connection and are able to implement local and system control.
With local manual control, the operator is directly at the intersection, observing the movement of vehicles and pedestrians. With a system one, it is located at the control point, i.e., far from the control object, and to provide it with information about traffic conditions, communication means and special means of displaying information can be used. The latter are made in the form of luminous maps of the city or its regions - mnemonic diagrams, devices for outputting graphic and alphanumeric information onto a cathode ray tube using a computer - displays and television systems that allow direct observation of the controlled area.
Local control is most often used at a separate or, as they say, isolated intersection, which has no connection with neighboring intersections either in terms of control or flow. The change of traffic lights at such an intersection is provided according to an individual program, regardless of traffic conditions at neighboring intersections, and the arrival of vehicles at this intersection is random.
The organization of a coordinated change of signals at a group of intersections, carried out in order to reduce the movement time of vehicles in a given area, is called coordinated control (control according to the “green wave” principle - SG). In this case, as a rule, system control is used.
Any automatic control device operates in accordance with a certain algorithm, which is a description of the processes of processing information and generating the necessary control action. In relation to road traffic, information about traffic parameters is processed and the nature of control of traffic lights affecting traffic flow is determined. The control algorithm is technically implemented by controllers that switch traffic light signals according to a prescribed program. In automated control systems using a computer, the algorithm for solving control problems is also implemented in the form of a set of programs for its operation.

Automated traffic control systems (ATCS) are an interconnected set of technical, software and organizational measures that collect and process information about traffic flow data and, on the basis of this, optimize traffic control. The task of automated traffic control systems (ATCS) is to ensure road safety organizations on the roads.

Automatic traffic control systems are divided into several types:

Mainline automated traffic control systems (ATCS) of coordinated control - centerless, centralized and centralized intelligent.

• · centerless ATCS - there is no need to create a control center. There are 2 modifications of centerless automated traffic control systems. In one of them, the work is synchronized by the main controller, to which there is communication from the other controllers (one line for all). In the next modification of centerless ATCS, all controllers have their own communication line.
• · centralized ATCS - have a control center, with controllers connected to it by their own communication lines. Often, ATCS can carry out multi-program CG with changing programs during the day.
• · centralized intelligent automated traffic control systems - they are equipped with transport identifiers, and depending on the traffic load, they can change traffic coordination plans.

City-wide automated traffic control systems (ATCS) - simplified, intelligent, with traffic control on city roads of continuous traffic and with reverse traffic.

· intelligent automated traffic control systems - contain powerful control computer complexes (UCCs), and a network of changing information displays. These ATCS can carry out continuous monitoring of traffic flow and can manage automatic adaptive traffic control and allow the redistribution of traffic flows across the network.

ACS, as part of the ITS, performs control and information functions, the main of which are:

• · traffic flow management;
• · provision of transport information;
• · organization of electronic payments;
• · security management and management in special situations.

In general, ACS subsystems can be presented as a set of road telematics devices, controllers and automated workstations (AWS), included in a data exchange network, with the organization of central and local control centers - depending on the density and intensity of road traffic.

Variable information signs (VIS), multi-position road signs, variable information boards (VIP), vehicle detectors, automatic road weather stations (ADMS), video cameras, etc. are used as road telematics devices.

The telecommunications part of the automated traffic control system is the road integrated communication system. The stable functioning of communication systems on highways makes it possible to increase the level of road safety and ensure the effective operation of road maintenance services, as well as operational and rescue services in the event of emergency situations.

The following functional subsystems can be organized as part of DISS:

• · information exchange of ACS DD;
• · communications with mobile objects (includes subsystems of operational-technological radio communications and radio access);
• · management and technical operation;
• · ensuring information security of DISS;
• · provision of information and communication services on a reimbursable basis.

Increasing the efficiency of traffic management is associated with the creation of automated traffic control systems (ATCS), which are integral components of intelligent transport systems (ITS). ITS is a comprehensive information support and management system for land road transport, based on the use of modern information and telecommunication technologies and management methods.

To ensure the functioning of automated traffic control systems and the provision of information and communication services to road users, DISS are created, which are currently subject to the following general requirements:

• · multifunctionality;
• · sustainability;
• · profitability.

ACS "CITY-DD" - is designed to ensure effective control of the movement of transport and pedestrian flows in cities using means, traffic light signaling, video monitoring and recording of violations on the roads, operational analysis of the environmental situation in the city, control of the movement of route transport, etc.

The main advantages and benefits of the ACS "CITY-DD"

• - a significant increase in the efficiency of traffic management and monitoring the state of affairs on the roads, which allows annual savings of about 5-8 million dollars per year throughout the regional center (savings consist of reduced fuel consumption, reduced travel time for vehicles, time spent by passengers on the road, etc. .d.);
• - more effective use of organizational and preventive measures to normalize traffic on the roads;
• - an integrated approach to traffic management;
• - use of domestic hardware and software aimed at modern technologies And modern methods traffic management in accordance with the requirements of ISO 9001;
• - new opportunities for monitoring the state of affairs on the roads: visual monitoring of city intersections, video recording of road accidents, video recording of violations of speed limits and intersection rules, operational analysis of the environmental situation, etc.;
• - the possibility of phased commissioning, through the gradual replacement of existing traffic control systems with expired service life and full compatibility of any part of the proposed system (controllers, control center, MZTs) with all types of existing equipment.

Automated system "City-DD":

• · Central control point;
• · Modules of zone centers (if necessary);
• · Controllers (in three versions - S, SM, SL);
• · Additional equipment;
• · Software package.

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Introduction

The increase in the number of cars, and as a result, the increase in their number on the roads of large cities is becoming more and more important issue to date. A large concentration of centers of attraction for the masses of people in the center of most megacities leads to the complication of managing the road network and increasing the cost of its maintenance. Many cities around the world cannot cope with daily transport challenges and face many kilometers of traffic jams day after day.

At the same time, the population's need for transportation continues to grow. Consequently, without proper measures, the situation is heading towards a dead end. UDS designed for a lighter load cannot cope and require modernization and optimization. Today, the city requires not only good, well-designed and then built roads, but also their quality management. Also, in many ways, previous methods of traffic management are becoming outdated and cannot keep up with the growing city, and multidirectional flows require dynamic management and integration of innovative systems to improve the transport situation, and in particular in Moscow. The entire system of construction of road networks and its management needs to be changed through new technologies, including mathematical modeling, which makes it possible to predict the behavior of road networks, make adjustments to its configuration, and much more. That is why the need for alternative, as well as any additional sources of information about the state of traffic, is sharply increasing. The latest complexes and systems for collecting and processing data are already being implemented.

The first chapter provides a brief analysis of the current transport situation in the city of Moscow, an analysis of the receipt and use of vehicle metric data using the Yandex.Traffic service, and an analysis of the usefulness of such data and the possibility of their use. At the end of the chapter, theoretical information is provided about roads, their classification, as well as what traffic flows are and their main characteristics, as well as the formulation of the problem

In the second chapter, an “experimental” section of the road network was selected, its main problems were considered using the Yandex.Traffic heat map, and also, based on the formulation of the problem, measures were proposed to improve the transport situation in this section of the road network.

The third chapter provides a detailed justification for the proposed changes using computer modeling and comparison of two UDS models and their parameters. A computer model was created based on the actual selected site, problems and data were analyzed, after which a computer model was created with the changes proposed in the second chapter. A comparative analysis of the data from the two models was carried out, allowing us to conclude that the changes made will lead to an improvement in traffic in this area.

The object of the study is traffic flows on the city road network.

The subject of the study is the possibility of using computer modeling to solve real practical problems.

The scientific hypothesis consists in the assumption of the possibility of using real data in a computer model, with its further (model) modernization, and obtaining improvement results, with high probability that are reliable and applicable in practice

The purpose of the study is to consider one of the problematic radical highways of Moscow, create a computer model of it, compare the behavior of the model with the picture in practice, make improvements and changes to the structure of the road network and further model the modified road network in order to confirm the improvement of the situation in this area.

The reliability of the results of the research carried out in the work is ensured by experimental confirmation of the main hypothesis, the consistency of the results of theoretical research obtained on the basis of the analysis of the developed mathematical models for calculating the main parameters of the UDS, with the results of the research.

1 Analysis of the current situation and problem statement

1.1 Justification of the relevance of the problem

It's no secret that many large cities around the world are experiencing huge problems in the transport sector. Transport in a metropolis plays a huge connecting role, which is why the transport system of a metropolis must be balanced, easily manageable and quickly respond to all changes in traffic within the city. In fact, a metropolis is an urban agglomeration with a huge concentration of cars and people, in which road transport (personal and public) plays a huge role, both in the movement of the population itself and in general logistics. That is why competent management of the transport system of a metropolis plays a huge role in its activities.

The population's need for transportation, both through public transport and personal cars, is growing every day. It is logical to assume that with an increase in the number of transport in a metropolis, the number of roads, interchanges and parking lots should increase proportionally, however, the development of the road transport network (RTN) does not keep pace with the pace of motorization.

Let us recall that according to statistics, the number of cars per capita is growing steadily (Figure 1.1).

automotive traffic flow computer

Figure 1.1 Number of cars per 1000 people in Moscow

At the same time, the Moscow City Road Service is not ready for such a rate of growth in motorization in the city. In addition to personal transport in the city, the problem of public transport and passenger transportation in Moscow must be solved. According to the state transport program, only 26% of passenger traffic comes from personal transport and 74% from public transport. At the same time, the total annual traffic volume in 2011 amounted to 7.35 billion passengers, and according to forecasts it will grow, and in 2016 it will amount to 9.8 billion passengers per year. It is planned that only 20% of this number of passengers will use personal transport. At the same time, in total, personal and above-ground public transport accounts for more than half of passenger traffic in Moscow. This means that solving the problems of road transport in a metropolis plays a big role for its normal functioning and the comfortable living of its residents. These data mean that without taking adequate measures to improve the transport situation in Moscow, we will face a transport collapse, which has been slowly brewing in Moscow in recent years.

It is also worth noting that in addition to the problems associated with the intra-city movement of passengers, the problem of transport flows of pendulum labor migration, and the flow of vehicles (mainly freight) going through the city is clearly visible. And if the problem of transit freight transport is partially solved by banning the entry and movement of trucks with a carrying capacity of over 12 tons in the city during the daytime, then the problem of moving passengers from the region to the city is much deeper and more difficult to solve.

This is facilitated by several factors, primarily the location of the centers of attraction of the human masses within the city limits. In particular, the location of a huge number of workplaces and offices of a large number of companies, the location of a large number of infrastructure, cultural and service facilities (in particular shopping centers, however, the trend towards their construction within the city limits is steadily decreasing in favor of their location outside the Moscow Ring Road). All this leads to the fact that huge flows of people move from the region to the city limits every day during the morning rush hour and back to the region in the evening. This problem is especially acute on weekdays, when a huge number of people rush to work in the morning rush hour and home in the evening rush hour. All this leads to a colossal load on the outbound routes, which are used during these hours by a huge number of passengers traveling both by public and personal transport. In addition, in the summer they are joined by summer residents, who create huge traffic jams on highways into the region every weekend, and out of it after the weekend.

All these problems require an immediate solution, through the construction of new roads and interchanges, the transfer of centers of attraction for the human masses and the optimization of the management of the existing road network structure. All of these decisions are simply not possible without careful planning and modeling. Because with the help of application programs and modeling tools we can see what effect we can achieve by implementing certain solutions, and choose the most suitable ones based on their cost assessment and the positive effect on the traffic flow.

1.2 Analysis of the current transport situation in Moscow using the Yandex Traffic Jams web service

Considering in more detail the problems outlined above, we must turn to existing telemetric systems for collecting information about the transport situation in Moscow, which could clearly show the problem areas of our metropolis. One of the most advanced and useful systems in this area, which has proven its effectiveness, is the Yandex Traffic Jams web service, which has proven its effectiveness and information content.

By analyzing the data provided by the service in the public domain, we can conduct data analysis and provide factual justification for the problems outlined above. Thus, we can clearly see areas with a tense transport situation, visually examine trends in the formation of congestion and propose a solution to the problem by selecting the most optimal mathematical model for solving the problem of modeling a specific problem area, with further obtaining results based on which it is possible to draw conclusions about the possibility of improvement transport situation in this particular case. In this way, we can combine the theoretical model and the real problem by providing a solution.

1.2.1 Brief information about the Yandex Traffic Jams web service

Yandex traffic jams is a web service that collects and processes information about the transport situation in Moscow and other cities in Russia and the world. Analyzing the information received, the service provides information about the transport situation (and for large cities it also provides a “score” for the congestion of the transport network), allowing motorists to correctly plan their trip route and estimate the expected travel time. The service also provides a short-term forecast of the expected traffic situation at a specific time, on a specific day of the week. Thus, the service is partially involved in TP optimization, allowing drivers to choose detour routes that are not covered by congestion.

1.2.2 Data sources

For clarity, let’s imagine that you and I are in an accident on Strastnoy Boulevard in front of Petrovka (small and without casualties). With our appearance we blocked, say, two rows out of the existing three. Motorists who were moving along our rows are forced to go around us, and drivers moving along the third row are forced to let those going around us pass. Some of these motorists are users of the Yandex.Maps and Yandex.Navigator applications, and their mobile devices transmit data about vehicle movement to Yandex.Traffic. As the users' cars approach our accident, their speed will decrease, and the devices will begin to “inform” the service about the traffic jam.

To participate in data collection, a motorist needs a navigator and the Yandex.Traffic mobile application. For example, if an accident occurs on the road, then some conscientious driver, having seen our accident, can warn other motorists about it by placing the appropriate dot in mobile Yandex.Maps.

1.2.3 Track processing technology

GPS receivers allow errors when determining coordinates, which makes it difficult to build a track. The error can “shift” the car several meters in any direction, for example, onto the sidewalk or the roof of a nearby building. Coordinates received from users are sent to electronic circuit city, which very accurately displays all buildings, parks, streets with road markings and other city objects. Thanks to this detail, the program understands how the car actually moved. For example, in one place or another the car could not enter the oncoming lane, or the turn was made according to the road markings without “cutting” the corner. (Figure 1.2)

Figure 1.2 Track processing technology

Consequently, the more users the service has, the more accurate the information about the traffic situation.

After combining the verified tracks, the algorithm analyzes them and assigns “green”, “yellow” and “red” ratings to the corresponding road sections.

1.2.4 Data merging

Next comes aggregation - the process of combining information. Every two minutes, the aggregator program collects, like a mosaic, information received from mobile Yandex.Maps users into one diagram. This diagram is drawn on the “Traffic” layer (Figure 1.3) of Yandex.Maps - both in the mobile application and in the web service.

Figure 1.3 Displaying traffic jams in Yandex.Maps

1.2.5 Point scale

In Moscow, St. Petersburg and other large cities, the Yandex.Traffic service evaluates the situation on a 10-point scale (where 0 points means free traffic, and 10 points means the city is “stopping”). With this estimate, drivers can quickly understand approximately how much time they will lose in traffic jams. For example, if the average score in Kyiv is seven, then the journey will take approximately twice as long as with free traffic.

The point scale is set up differently for each city: what is a minor problem in Moscow, is a serious traffic jam in another city. For example, in St. Petersburg, with six points, a driver will lose approximately the same amount of time as in Moscow with five. Points are calculated as follows. Routes along the streets of each city are pre-designed, including main highways and avenues. For each route there is a reference time during which it can be driven on a free road without breaking the rules. After assessing the overall congestion of the city, the aggregator program calculates how much the real time from the reference one. Based on the difference on all routes, the load in points is calculated. (Figure 1.4)

Figure 1.4 Generalized diagram of the operation of the Yandex.Traffic portal

1.3 Using information obtained using the YandexTraffic web service to find problem areas in the road network

Summarizing the information received, we can come to the conclusion that the service provides a very useful information(both online and in forecast mode) about the transport situation in Moscow and other regions, which can be used for scientific purposes, in particular to identify problem areas, streets and highways, and forecast congestion. Thus, we can identify primary problems both in the entire road network as a whole and in its individual sections, and substantiate the existence of certain transport problems in the road network by analyzing the information obtained using this web service. Based on the primary analytics data, we can build a primary picture of the difficulties at the road network. Then, using modeling tools and specific data, confirm or refute the presence of a particular problem, and then try to build a mathematical model of the road traffic system with changes made to it (change the traffic light phases, model a new interchange in the problem area, etc.) and propose an option (s) improving the situation in a given area. Then select the most suitable solution from the point of view of the ratio of efficiency and cost assessment.

1.4 Search and classification of problems using the Yandex.Traffic web service

This web service can be considered as one of the methods for improving traffic management (hereinafter referred to as traffic control) in Moscow. Based on the information from the portal, we will try to assess problem areas in the Moscow road traffic system and propose systemic solutions to improve the road traffic system, as well as identify trends in congestion.

Considering the portal data, we must conduct a daily analysis of changes in traffic congestion in Moscow and identify the most problematic areas. The most suitable for these purposes are peak hours, when the load on the road network is maximum.

Figure 1.5 Average congestion of the main radial highways of Moscow by hour on weekdays

To confirm the hypothesis about the congestion of the road network and the presence of the problem of labor commuting, we will analyze the data as a general gene. the Moscow plan with a “layer” of traffic jams applied, as well as individual problem areas and consider the dynamics of their movement.

The vast majority of jobs in Moscow start labor activity at 8-00 - 10-00 Moscow time, in accordance with the labor code, the duration of the working day with a five-day work week (the most common option) is 8 hours, so we can assume that the main load is on the traffic police, in accordance with the hypothesis about commuting labor migration (MLM) should occur during periods of time, in the morning hours: from 6-00 (region - MKAD) and until 10-00 (closer to the main places of concentration of jobs in Moscow) and from 16-00 - 18- 00 (center) to 20-00 (radial routes for departure) in the evening.

Figure 1.6 At 6-00 there are no difficulties on the road traffic system

Figure 1.7 Difficulties when approaching Moscow

Based on the analytics, at 7-00 we have difficulties approaching the city on the main thoroughfares to the center.

Figure 1.8 Difficulties in the south of Moscow

Figure 1.9 Difficulties in the southwest

A similar picture is observed on absolutely all radial highways of the capital without exception. Maximum score in the morning hours was reached at 9:56 Moscow time, by this time congestion had shifted from the outskirts of the city to its center.

Figure 1.10 9-00 - 9-56 morning peak load on the road network

Figure 1.11 TTR at 16-00

An improvement in the transport situation in general was observed until 15-40 Moscow time, the situation “in the center” did not deteriorate until the end of the day. The general situation tended to begin to deteriorate from 16-00, while the situation began to improve at approximately 20-00 Moscow time. (Appendix A). On weekends, there are practically no problems on the road traffic system, and according to the gradation of the Yandex.Traffic portal, the “score” did not exceed “3” for the entire period of daily observation. Thus, we can confidently state that the city is congested due to the concentration of centers of attraction of the human masses (jobs) in its center, and a much better picture on weekends, when the MTM problem is absent.

Drawing intermediate conclusions, we can say with confidence that the main direction of work should be reducing the number of centers of attraction for human masses in the city center and limiting travel to this area, as well as increasing the capacity of the main radial highways. Already, the Moscow government is taking steps in this direction, by introducing paid parking in the center of Moscow and introducing a pass-through system for entering the city center for vehicles (hereinafter referred to as vehicles) with a total weight of over 3.5 tons.

Figure 1.12 Paid parking zone in Moscow

Analyzing the findings, we can conclude that traffic difficulties have a unidirectional format on weekdays and the same dynamics of beginning and end (in the morning from the region, gradually moving towards the city center, and vice versa in the evening - from the center towards the region.

Thus, considering this trend, we can conclude that the introduction of dynamic traffic control is vital, since road congestion is unidirectional. Using intelligent systems, we can change the capacity of the road in one direction or another (for example, using a reversible lane “turning on” it in the direction that has insufficient capacity), change and adjust the phases of traffic lights to achieve maximum capacity in areas with difficulties . Such systems and methods are becoming increasingly widespread (for example, the reversible lane on Volgogradsky Prospekt). At the same time, it is impossible to “blindly” increase the capacity of problem areas, since we can simply push the congestion to the first place with insufficient capacity. That is, the solution to transport problems should be comprehensive, and modeling of problem areas should not occur in isolation from the entire road traffic system and should be carried out comprehensively. Thus, one of the goals of our work should be the modeling and optimization of one of the problematic radial highways of Moscow.

1.5 Theoretical information

1.5.1 Classification of roads in Russia

Decree of the Government of the Russian Federation dated September 28, 2009 N 767 approved the Rules for the classification of highways in the Russian Federation and their classification into categories of highways.

Based on traffic conditions and access to them, highways are divided into the following classes:

· motorway;

· expressway;

· regular road (not expressway).

1.5.2 Highways depending on the estimated traffic intensity

According to SNiP 2.05.02 - 85 as of July 1, 2013 are divided into the following categories (Table 2):

Table 2

	Estimated traffic intensity, given units/day.
IA (motorway)
IB (highway)
Ordinary roads (non-express roads)
		St. 2000 to 6000
		St. 200 to 2000

1.5.3 Main parameters of TP and their relationship

Traffic flow (TP) is a set of vehicles simultaneously participating in traffic on a certain section of the road network

The main parameters of the traffic flow are:

flow speed?, flow intensity l, flow density c.

Speed? Transport flow (TP) is usually measured in km/h or m/s. The most commonly used unit of measurement is km/h. Flow speed is measured in two directions, and on a multi-lane road, speed is measured in each lane. To measure the flow speed on the road, sections are taken. The road section is a line perpendicular to the axis of the road, passing through its entire width. The speed of the TP is measured in a section or section.

A section is a section of road enclosed between two sections. The distance L, m between sections is chosen in such a way as to ensure acceptable speed measurement accuracy. The time t is measured, from the time the car passes the section - the time interval. Measurements are carried out for a given number n of cars and the average time interval is calculated?:

Calculate the average speed on the section:

V = L/?.

That is, the speed of a traffic flow is the average speed of cars moving in it. To measure the speed of a TP in a cross-section, remote speed meters (radar, lamp - headlight) or special speed detectors are used. Speeds V are measured for n cars and the average speed on the section is calculated:

The following terms are used:

Average temporary speed V - average speed of vehicles in the section.

Average spatial speed? - the average speed of vehicles traveling over a significant section of the road. It characterizes the average speed of traffic flow on the site at some time of the day.

Travel time is the time required for a car to travel a unit length of road.

Total mileage is the sum of all vehicle paths on a road section for a given time interval.

The speed of movement can also be divided into:

Instantaneous Va - speed recorded in individual typical sections (points) of the road.

Maximum Vm - the highest instantaneous speed that a vehicle can develop.

The traffic intensity l is equal to the number of cars passing the road section per unit time. At high traffic intensities, uses shorter time intervals.

Traffic intensity is measured by counting the number n of cars passing through a road section in a given unit of time T, after which the quotient l = n/T is calculated.

Additionally, the following terms are used:

Traffic volume is the number of vehicles crossing a road section in a given unit of time. Volume is measured by the number of cars.

Hourly traffic volume is the number of vehicles passing through a road section during an hour.

The density of traffic flow is equal to the number of cars located on a section of road of a given length. Usually 1 km sections are used, the density of cars per kilometer is obtained, sometimes shorter sections are used. Density is usually calculated from the speed and intensity of traffic flow. However, density can be measured experimentally using aerial photography, towers or tall buildings. Use Extra options, characterizing the density of traffic flow.

Spatial interval or briefly interval lп, m - the distance between the front bumpers of two cars following each other.

Average spatial interval lп.ср - average value of intervals lп on the site. The interval lп.ср is measured in meters per car.

The spatial interval l p.sr, m is easy to calculate, knowing the flow density c, cars/km:

1.5.4 Relationship between traffic flow parameters

The relationship between speed, intensity and density of traffic is called the basic equation of traffic flow:

V ?s

The main equation relates three independent variables, which are the average values ​​of the traffic flow parameters. However, in real road conditions, the variables are interrelated. As the speed of traffic flow increases, traffic intensity first increases, reaches a maximum, and then decreases (Figure 1.13). The decrease is due to an increase in the intervals lп between cars and a decrease in the density of traffic flow. At high speeds, cars quickly pass through sections, but are located far from each other. The goal of traffic control is to achieve maximum flow intensity, not speed.

Figure 1.13 Relationship between TP intensity, speed and density: a) dependence of TP intensity on speed; b) dependence of TP density on speed

1.6 Transport modeling methods and models

Mathematical models used to analyze transport networks can be classified based on the functional role of the models, that is, on the tasks in which they are used. Conventionally, 3 classes can be distinguished among the models:

· Forecast models

· Simulation models

· Optimization models

Predictive models are used when the geometry and characteristics of the road network and the location of flow-generating objects in the city are known, and it is necessary to determine what the traffic flows in this network will be. In detail, the traffic load forecast includes the calculation of average traffic indicators, such as the volume of inter-district movements, flow intensity, distribution of passenger flows, etc. Using such models, it is possible to predict the consequences of changes in the transport network.

Unlike predictive models, simulation modeling has the task of modeling all the details of the movement, including the development of the process over time.

This difference can be formulated very simply if predictive modeling answers the questions of “how much and where” vehicles will move in the network, and simulation models answer the question of how detailed the movement will occur if “how much and where” is known. Thus, these two directions of transport modeling are complementary. From the above it follows that the class of simulation models, according to their goals and tasks performed, includes a wide range of models known as traffic flow dynamics models.

Dynamic models are characterized by a detailed description of movement. The area of ​​practical application of such models is improving the organization of traffic, optimizing traffic light phases, etc.

Flow forecasting models and simulation models have the main goal of reproducing the behavior of traffic flows close to real life. There is also a large number of models designed to optimize the functioning of transport networks. In this class of models, problems of optimizing passenger transportation routes, developing an optimal configuration of a transport network, etc. are solved.

1.6.1 Dynamic traffic flow models

Most dynamic models of traffic flows can be divided into 3 classes:

· Macroscopic (hydrodynamic models)

Kinetic (gas dynamic models)

Microscopic models

Macroscopic models are models that describe the movement of cars in average terms (density, average speed, etc.). In such transport models, the flow is similar to the movement of a fluid, which is why such models are called hydrodynamic.

Microscopic models are those in which the movement of each vehicle is explicitly modeled.

An intermediate place is occupied by the kinetic approach, in which the traffic flow is described as the distribution density of cars in phase space. A special place in the class of micromodels is occupied by models such as cellular automata, due to the fact that these models adopt a highly simplified discrete description of the movement of cars in time and space, because of this, high computational efficiency of these models is achieved.

1.6.2 Macroscopic models

The first of the models is based on a hydrodynamic analogy.

The main equation of this model is the continuity equation, expressing the “law of conservation of the number of cars” on the road:

Formula 1

Where is the density, V(x,t) is the average speed of cars at a point on the road with coordinate x at time t.

The average speed is assumed to be a deterministic (decreasing) function of density:

Putting into (1) we obtain the following equation:

Formula 2

This equation describes the propagation of nonlinear kinematic waves with transfer speed

In reality, the density of cars, as a rule, does not change abruptly, but is a continuous function of coordinates and time. To eliminate jumps, a second-order term describing density diffusion was added to equation (2), which leads to a smoothing of the wave profile:

Formula 3

However, the use of this model is not adequate to reality when describing nonequilibrium situations that arise near road inhomogeneities (on and off ramps, narrowings), as well as under conditions of so-called “stop-and-go” traffic.

To describe nonequilibrium situations, instead of deterministic relation (3), it was proposed to use a differential equation to simulate the dynamics average speed.

The disadvantage of Payne's model is its stability to small disturbances at all density values.

Then the velocity equation with this replacement takes the form:

To prevent discontinuities, a diffusion term is added to the right side, an analogue of viscosity in the hydrodynamic equations

Instability of stationary homogeneous solution at density values ​​exceeding the critical one, it allows one to effectively simulate the occurrence of phantom congestion - stop-and-go modes in a homogeneous flow that arise as a result of small disturbances.

The macroscopic models described above are formulated mainly on the basis of analogies with the equations of classical hydrodynamics. There is also a way to derive macroscopic models from a description of the process of interaction between cars at the micro level using a kinetic equation.

1.6.3 Kinetic models

Unlike hydrodynamic models, which are formulated in terms of density and average flow velocity, kinetic models are based on a description of the dynamics of phase flow density. Knowing the time evolution of the phase density, it is also possible to calculate the macroscopic characteristics of the flow - density, average speed, velocity variation and other characteristics that are determined by the moments of phase density at speeds of various orders.

Let us denote the phase density as f (x, v, t). The usual (hydrodynamic) density с(x, t), the average velocity V(x, t) and the velocity variation И(x, t) are related to the moments of phase density by the relations:

1) The differential equation that describes the change in phase density with time is called the kinetic equation. The kinetic equation for traffic flow was first formulated by Prigogine and co-authors in 1961 in the following form:

Formula 4

This equation is a continuity equation expressing the law of conservation of cars, but now in phase space.

According to Prigogine, the interaction of two cars on the road refers to the event in which a faster car overtakes a slower car in front. The following simplifying assumptions are introduced:

· the opportunity for overtaking is found with a certain probability p; as a result of overtaking, the speed of the overtaking car does not change;

· the speed of the car in front does not change in any case as a result of interaction;

· interaction occurs at a point (the size of the cars and the distance between them can be neglected);

· the change in speed as a result of interaction occurs instantly;

· Only paired interactions are considered; simultaneous interactions of three or more vehicles are excluded.

1.7 Problem statement

In the current study, we use static data on traffic jams using the Yandex.Traffic service as basic information. Analyzing the information received, we come to the conclusion that the Moscow city traffic system cannot cope with transport traffic. Difficulties identified at the stage of analysis of the data obtained allow us to conclude that most of the difficulties at the road transport system take place exclusively on weekdays, and are directly related to the phenomenon of “MTM” (commuting labor migration), since during the analysis of difficulties on weekends And holidays was not identified. Difficulties on weekdays include the appearance of an avalanche spreading from the outskirts of the city to its center, and the presence of the opposite effect in the afternoon, when the “avalanche” goes from the center to the region. In the morning hours, difficulties begin to be observed on the outskirts of Moscow, gradually spreading into the city. It is also worth noting that the “decoupling” of radial highways will not lead to the desired effect, since, as can be seen from the analysis, the “entrance” to the city holds back congestion at a certain time interval, due to which the central part of the city travels in optimal mode for some time . Then, given the same difficulties, congestion forms in the MKAD-TTK area, while congestion at the entrances continues to increase. This trend is happening all the time morning time. At the same time, the opposite direction of movement is completely free. From this it follows that the control system for traffic lights and traffic direction must be dynamic, changing its parameters to suit the current situation on the road.

The question arises about the rational use of road resources and the implementation of such opportunities (changing traffic light phases, reversing lanes, etc.).

At the same time, it is impossible to limit ourselves to this, since this “global traffic jam” has no end point. These actions should be implemented only in conjunction with restrictions on entry into Moscow and the center, in particular for residents of the Moscow region. Since, in fact, based on the analysis, all problems are reduced to MTM flows, they must be competently redistributed from personal transport to public transport, making it more attractive. Such measures are already being introduced in the center of Moscow (paid parking, etc.). This will relieve congestion on city roads during rush hours. Thus, all my theoretical assumptions are built with a “reserve for the future”, and the condition that the congestion will become finite (the number of passenger flows to the center will decrease), passenger flow will become more mobile (one bus with 110 passengers occupies 10-14 meters of road surface, versus 80 -90 units of personal transport, with the same number of passengers occupying 400-450 meters). In a situation where the number of people entering will be optimized (or at least reduced as much as possible based on economic and social opportunities), we will be able to apply two assumptions on how to improve the management of traffic networks in Moscow without investing large amounts of money and computing power, namely:

· Use analytical and modeling data to identify problem areas

· Developing ways to improve road traffic and its management in problem areas

· Creation of mathematical models with proposed changes and their further analysis for efficiency and economic feasibility, with further introduction into practical use

Based on the above, with the help of mathematical models we can quickly respond to changes in the road network, predict its behavior and adjust its structure to them.

Thus, on a radial highway, we can understand the reason why it operates in an abnormal mode and has traffic jams and congestion along its length.

Thus, the problem statement based on the problem consists of:

1. Analysis of one of the radial highways for the presence of difficulties, including peak hours.

2. Creating a model of a part of this radial highway in the place of greatest difficulties.

3. Introduction of improvements to this model based on UDS analytics using real data and modeling data, and creating a model with the changes made.

2 Creation of an improved version of the UDS

Based on the formulation of the problem and analysis of transport difficulties in Moscow, to create a practical model, I chose a section of a branch of one of the radial highways (Kashirskoye Highway), in the section from the intersection of Andropov Avenue and Kolomensky Proezd to the “Torgovy Tsentr” stop. The reason for the choice is many factors and in particular:

· The tendency for congestion to form in the same places with the same tendency

· Vivid picture of “MTM” problems

· Availability of solvable points and the ability to simulate traffic light regulation in a given area.

Figure 1.14 Selected area

The selected area has characteristic problems that can be modeled, namely:

· The presence of two problem points and their cross-influence

· The presence of problem points, changing which will not improve the situation (possibility of using synchronization).

· A clear picture of the impact of the MTM problem.

Figure 1.15 11-00 problems to the center

Figure 1.16 Problems from the center. 18-00

Thus, in this area we have the following problem points:

· Two pedestrian crossings equipped with traffic lights in the Nagatinskaya floodplain

· Traffic light at the intersection of Andropov Avenue and Nagatinskaya Street

Nagatinsky metro bridge

2. Creation of an improved version of the UDS

2.1 Site analytics

The length of traffic jams on Andropov Avenue is 4-4.5 km in each of the 2 directions (in the morning to the center - from Kashirskoye Highway to the second pedestrian crossing in the Nagatinskaya floodplain, in the evening to the region - from Novoostapovskaya street to Nagatinskaya street). The second indicator, the speed of traffic during peak hours, does not exceed 7-10 km/h: it takes about 30 minutes to travel a 4.5 km section during peak hours. As for the duration, traffic jams to the center on Andropov Avenue begin at 7 am and last until 13-14 hours, and traffic jams to the region usually start at 15 and last until 21-22 hours. That is, the duration of each of the “rush hours” on Andropov is 6-7 hours in each of the 2 directions - an prohibitive level even for Moscow, which is accustomed to traffic jams.

2.2 Two main reasons for the formation of traffic jams on Andropov Avenue

Reason one: the avenue is overloaded with excess “over-traffic” traffic. From the Nakhimovsky Prospekt metro station to the center of the residential part of Pechatniki, the straight line is 7.5 kilometers. And on the roads there are 3 routes from 16 to 18 kilometers. Moreover, two of the three routes pass through Andropov Avenue.

Figure 2.1

All these problems are caused by the fact that between the Nagatinsky and Brateevsky bridges there are 7 km in a straight line, and 14 km along the Moscow River. There are simply no other bridges or tunnels in this gap.

Reason two: the low capacity of the avenue itself. First of all, traffic is slowed down by a dedicated lane created several years ago, after which only 2 lanes remained for traffic in each direction. Three traffic lights (transport in front of Nagatinskaya Street and two pedestrian ones in the Nagatinskaya floodplain) also greatly contribute to congestion.

2.3 Strategic decisions on Andropov Avenue

To solve the problem of overruns, it is necessary to build 2-3 new connections between the Nagatinsky and Brateevsky bridges. These transport connections will eliminate overruns and make it possible to manage traffic, stimulating not the “center-periphery” flow, but the “periphery-periphery” flow.

The problem is that building such facilities is very time-consuming and expensive. And each of them will cost billions of rubles. Thus, if we want to improve something here not in 5 years, but in a year or two, the only way is to work with the capacity of Andropov Avenue. Unlike the construction of new bridges and tunnels, this is much faster (0.5-2 years) and 2 orders of magnitude cheaper (50-100 million rubles). Because the avenue’s capacity can be increased through inexpensive local “tactical” measures in the most problematic areas. This will ensure existing demand, improve all traffic indicators: reduce the length of traffic jams, shorten the duration of rush hours, increase speed.

2.4 Tactical measures on Andropov Avenue: 4 groups

2.4.1 Stage 1. Traffic light regulation

There are 3 traffic lights on the problem area: two pedestrian ones in the Nagatinskaya floodplain and one transport one at the intersection of Andropov and the street. New items and Nagatinskaya.

Two pedestrian traffic lights in the Nagatinskaya floodplain are already operating in the maximum “extended” mode (150 seconds for transport, 25 for pedestrians). An additional lengthening of the cycle is unlikely to be effective for transport, but will increase the already considerable wait for pedestrians. The only thing that can and should be done with traffic light regulation is to synchronize both pedestrian traffic lights so that vehicles spend less time accelerating and braking. This will have a slight effect towards the center during morning rush hour. Pedestrian traffic lights do not have much impact on traffic in both directions at other times and towards the region in the evening. But with the traffic light at the intersection of Andropov and st. New items and Nagatinskaya's situation is more interesting. It clearly keeps the flow towards the area during the evening rush hours. Then the transport travels along a mass of alternative streets (Nagatinskaya Embankment, Novinki Street, Nagatinskaya Street, Kolomensky Proezd, Kashirskoye Shosse and Proletarsky Prospekt).

Let's look at the current mode of operation of the traffic light and think about what can be done.

Figure 2.2 Traffic light phases

Figure 2.3 Current temporary mode of operation of the traffic light

Firstly, the cycle for an intersection with a main street is very short - only 110-120 seconds. On most highways, the cycle time during peak hours is 140-180 seconds, on Leninsky it is even 200.

Secondly, the operating mode of the traffic light varies extremely insignificantly depending on the time of day. Meanwhile, the evening flow is fundamentally different from the morning one: the forward flow along Andropov from the region is much smaller, and the left-turn flow from Andropov from the center is much greater (people return home to the Nagatinsky backwater).

Thirdly, for some reason the time of the forward phase during the day has been reduced. What is the point of this if the forward flow along Novinki and Nagatinskaya does not experience serious problems even during rush hours, and even more so during the day?

The solution suggests itself: equate the daytime regime to the morning one, and in the evening - slightly “extend” phase 3 (Andropov in both directions), and strongly extend the “fan” phase 4 (Andropov from the center straight, right and left). This will effectively free up both Andropov’s direct move and the “pocket” for those waiting to turn.

Figure 2.4 Proposed time-based traffic light operation

As for the morning rush hour, “pulling” Andropov at this intersection in the morning into the center is now pointless. The flow does not use the entire length of the “green phase” because it cannot quickly pass the intersection due to the traffic jam before narrowing on the bridge from 4 lanes to 2.

2.4.2 Re-partitioning

There are two problems with marking on Andropov:

- dedicated lane on 3-lane sections of Andropov Avenue

- incorrect markings at the intersection with Nagatinskaya Street and Novinki Street

It's no secret that the dedicated lane has sharply reduced the capacity of Andropov Avenue. This applies to movement both to the center and to the region. Moreover, passenger traffic along the dedicated lane is minimal and does not exceed several hundred people even during peak hours. This is not surprising: the dedicated lane runs along the “green” metro line, and there are almost no points of attraction at a distance from the metro along the avenue itself. The carrying capacity of each of the public lanes is about 1,200 people per hour. This means that the dedicated lane, contrary to its purpose, did not increase, but decreased the carrying capacity of Andropov Avenue.

Let me add: the passenger flow of ground transport on Andropov Avenue has a chance to decline further. After all, already in 2014 they plan to open the Technopark metro station in the Nagatinskaya floodplain. This will allow the majority of visitors to the Megapolis shopping center and those working in the Technopark to use the metro without transferring to ground transport.

It would seem that the entire allocation to Andropov would be cancelled, and that would be the end of it. But analysis and long-term observations have shown: the dedicated lane on Andropov Avenue does not interfere everywhere, but only in those areas where there are 3 lanes in one direction (2+A) and where this creates a “bottleneck.” Where there are 4 lanes in one direction (3+A), a dedicated lane does not interfere, but even allows for increased uniformity of traffic flows and serves as a lane for right turns, acceleration and braking.

Therefore, as a matter of priority, I propose to abolish the dedicated lane in narrow areas where it creates the greatest problems:

· towards the region on the Saikinsky overpass and Nagatinsky bridge, Saikin street

· towards the center along the entire section from the entrance to the Nagatinsky Bridge to the Saikinsky overpass inclusive.

Figure 2.5 Locations where lane deletion is required

Figure 2.6 Re-marking Andropov Avenue

It will also be necessary to cancel the dedicated lane towards the region in the section from Nagatinskaya Street to Kolomensky Proezd: the increased flow towards the region will not be able to fit into the existing 2 lanes. By the way, entry to the dedicated lane in this place is still allowed, but only for parking.

In addition to the dedicated lane, problems are caused by the incompetent marking of Andropov Avenue at the intersection with Nagatinskaya Street and Novinki Street.

Firstly, the width of the stripes is large, and their number is insufficient. With such a width of the roadway, it is easy to add a lane on each side.

Secondly, the markings, despite the widening of the intersection, for some reason divert all traffic into left-turn lanes, from where those traveling straight have to “push through” to the right.

However, the ineptitude of the designers is excusable: the junction is complex, the width of the roadway “walks”. This solution for this intersection also did not appear immediately. It allows you to increase the number of rows in the intersection area, and leave those driving straight in their lanes, “driving” the straight line a little to the right. As a result, the number of lane changes will decrease, and the speed of crossing the intersection will increase in both directions.

Figure 2.7 Proposed traffic management scheme at the Andropova - Nagatinskaya - Novinki intersection

Figure 2.8 Proposed traffic pattern at the intersection

Local broadenings

The next stage is proposed to carry out the now most necessary widening towards the center in the section from the Nagatinsky metro bridge to the exit to Trofimova Street. This would make it possible to return 3 lanes to private transport, giving the 4th to public transport - exactly the same as was done towards the region in this section.

Figure 2.9 Local broadenings

2.4.3 Construction of 2 off-street crossings in the Nagatinskaya floodplain

Construction of an overpass has recently begun in the area of ​​the South River Station stop near the Nagatinsky metro bridge. After its construction, the pedestrian traffic light will be dismantled.

Figure 2.10 Construction plan for the overpass

This could be great news, but there is nothing to rejoice at: 450 meters to the north there is another crossing opposite the Megapolis shopping center. The simultaneous construction of 2 crossings with the removal of both pedestrian traffic lights would give an excellent effect for the direction to the center: the throughput with the same width would increase by 30-35% due to the abolition of acceleration and braking in front of traffic lights. But they are not going to build an off-street crossing opposite the Megapolis shopping center, which means there is no way to remove the second traffic light. And the effect of one overpass will be insignificant - no more than the simple synchronization of two traffic lights. Because in both cases, acceleration and deceleration are preserved.

3 Justification of the proposed solutions

Based on analytics, we calculate problem points in a particular road network zone and, based on actually possible solutions, apply them. Since the program allows us not to do cumbersome calculations manually, we can use it to determine the optimal parameters of certain problem areas in the UDS, and after optimizing them, obtain the result of computer modeling, which can answer the question of whether the proposed changes will improve throughput. Thus, using computer modeling, we can check whether the proposed changes based on analytics correspond to the real situation, and whether the changes will have the expected effect.

3.1 Use of computer simulation

Using computer simulation, we can predict with a high degree of probability the processes occurring on the road network. In this way, we can conduct a comparative analysis of the models. Model the current structure of the UDS with its features, modernize and improve it, and create a new model based on the UDS with adjustments made to it. Using the data obtained, at the computer modeling stage we can get an answer as to whether it makes sense to make certain changes to the traffic flow system, as well as use modeling to identify problem areas.

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Introduction

The concept of adaptive traffic control at a transport network node

Comparison of time-dependent and transport-dependent traffic control strategies

Modeling setup and analysis

Development of a base of fuzzy rules, determination of control parameters for the movement of traffic flows in a transport network node

1 Construction of the membership function

2 Construction of rules for compliance with a specific class of control parameter

3 Base of fuzzy rules

Conclusion

Bibliography

Introduction

Changed conditions of mobility, characterized by an increase over the course of recent years the number of cars has led to increased load on transport infrastructure and the environment. The growing need for improved travel conditions cannot be fully satisfied (neither within settlements, or beyond) only by creating new transport routes or carrying out other construction activities. To get out of this situation, it is necessary to introduce a whole set of measures to organize and manage traffic. Adaptive traffic control systems (ATCS) represent new approach to the organization of traffic control and, together with the high-performance transport computers controlled by them, implement the appropriate control technologies.

The constant increase in the number of vehicles in conditions of insufficient road capacity leads to difficulties in the movement of traffic flows. Intelligent transport systems (ITS) make it possible to minimize the formation of congestion situations and increase the capacity of the transport network. Developments in the field of ITS are being used to organize traffic in populated areas and highways. Optimization of traffic management is achieved through the interaction of control, classifying, forecasting, expert, decision-making or ITS subsystems supporting these processes. In this regard, the task is to find methods for processing information about emergency situations on the road network (RDN).

This paper will consider the following issues: the concept of adaptive traffic control at a transport network node, on the network, as well as a comparison of time-dependent and transport-dependent traffic control strategies.

1. The concept of adaptive traffic control at a transport network node

The possibilities of improving traffic conditions through optimal traffic organization are largely underestimated, and the development of transport infrastructure is understood mainly as activities related to the construction of new roads and highways, reconstruction of existing overpasses and interchanges. At the same time, the introduction of modern innovative technologies, called “Intelligent Transport Systems” (ITS), can significantly improve the transport situation. The introduction of ITS technologies in Russia makes it possible to better manage traffic flows, increase the capacity of the road network and reduce the load on its individual elements.

The growth of the vehicle fleet and the volume of traffic leads to an increase in traffic intensity, which in the conditions of cities with historically developed buildings leads to the emergence of a transport problem. It is especially acute at the junction points of the road network. Here, transport delays increase, queues and congestion form, which causes a decrease in the speed of communication, unjustified excessive consumption of fuel and increased wear and tear on vehicle components and assemblies. Changing mobility conditions, characterized by an increase in the number of cars in recent years, have led to increased pressure on transport infrastructure and the environment. The growing need for improved travel conditions cannot be fully satisfied (neither within populated areas nor outside them) only by creating new transport routes or carrying out other construction activities. To get out of this situation, it is necessary to introduce a whole set of measures to organize and manage traffic.

Adaptive traffic control systems (ATCS) represent a new approach to organizing traffic control and, together with the high-performance transport computers controlled by them, implement appropriate control technologies. Currently, in world practice, the following technologies for managing traffic flows are most common as part of automated control systems:

Management technology based on fixed plans (coordinated management);

Network adaptive control technology;

Situational management technology.

SAUDD is a traffic control system with centrally distributed intelligence, consisting of:

central control point (CPU);

adaptive traffic control points equipped with intelligent controllers and vehicle detectors, providing:

local adaptive management of the most complex and important intersections and sections of road networks;

information interaction with the CPU;

system detectors that report information about traffic flows to the CPU;

system controllers controlled from the CPU continuously or periodically.

Traffic management at the level of traffic services represents a set of engineering and organizational measures on the existing road network, ensuring safety and sufficient speed of transport and pedestrian flows. Such activities include traffic management, which, being an integral part of traffic management, usually solves more specific problems. In general, management means influencing a particular object in order to improve its functioning. In relation to road traffic, the object of control is transport and pedestrian flows.

The essence of traffic control is to oblige drivers and pedestrians, prohibit or recommend them certain actions in the interests of ensuring speed and safety. It is carried out by including the relevant requirements in the Traffic Rules, as well as by using a set of technical means and administrative actions of road patrol inspectors and other persons with appropriate authority.

2. Comparison of time-dependent and transport-dependent traffic control strategies

The current state of traffic flow management in most cities can generally be characterized in such a way that control devices (nodes) are controlled according to a fixed schedule or according to the state of traffic flow. The significant difference is that time-based control does not require detectors and the system is unable to respond to any changes in traffic flow. In the case of traffic-dependent stop line control, there are detectors that detect the instantaneous presence of vehicles, and the control device thus responds to the instantaneous conditions in the node by increasing the duration of the green signal. Therefore, we are talking about control in a second time grid.

Time-dependent (autonomous) control - transport states are determined on the basis of a statistical analysis of historical values ​​of the characteristics of traffic flows (traffic intensity) and on their basis the output values ​​of the regulation process are determined.

Traffic-dependent (current time mode - online) control, in Anglo-Saxon literature, also called Traffic Responsive, is that the intervention of the control system is calculated based on the instantaneous traffic situation. Online mode methods provide real-time operation and, based on variable input data on the movement of traffic flows, change and optimize control parameters every second, i.e. duration of the green signal in the corresponding direction. Control devices in this mode operate independently or, in extreme cases, are located in a line and linearly coordinated.

Management is carried out at the local level. If a control center is used, then the status of control devices or the status of traffic flows is often monitored. Real-time control of traffic lights is quite well known and is commonly used under the name traffic-dependent control or dynamic control. Its principle is that a transport hub is usually equipped with two types of sensors: interval and call sensors, which are in most cases inductive loops. The transport control device controls according to a program that continuously tests the state of the traffic flow over individual sensors and, based on predetermined algorithms, increases the duration of signals, modifies the phase sequence or inserts a phase on call. These changes typically occur within predetermined cycle times and predetermined maximum green signal durations. The gap sensor, located approximately 30-50 m in front of the stop line, gets its name from the fact that it continuously measures the time intervals between vehicles and if they are less than this value (usually 3-5 seconds), it increases the duration of green signals up to a predetermined maximum. This measurement method is called Time Interval Measurement Control. The second possibility is that individual nodes are connected to a traffic control center, which coordinates and manages the operation of the nodes at the district level. The following modes are used to control the area:

Time-dependent (autonomous) control - information on the characteristics of the state of traffic flows in the area is obtained through statistical analysis, data on the characteristics of traffic flows (traffic intensity and composition) over the past years, measured at the main points of the transport network, and on their basis the mode is determined operation of transport control devices. They are then entered into control devices depending on the time of day or day of the year. The calculations optimize the duration of green signals, cycle duration and time shift. An example of an autonomous mode method is the TRANSYT method, where fictitious vehicles are “released” according to predetermined rules into an area and pass through that area based on and in accordance with the traffic flow pattern. Their movement is influenced by changes in the controlled parameters of the node. Using numerical mathematical methods for various parameters, such as cycle duration, green signal duration and time shift, the minimum of a certain objective function is found (parameter optimization).

Transport-dependent (online mode) control is characterized by the fact that for various states of traffic flows on the network, systems of signal plans are calculated in advance, which are stored in control devices or in the traffic flow control center. The TRANSYT method is typically used to calculate the maximum green duration, cycle length, and time offset values. At the same time, strategic sensors are selected in the area and logical equations are drawn up that describe different combinations of states of all or selected sensors. Depending on the current traffic situation, the program that best suits the given situation is selected through an appropriate equation. An example is the description of the state of traffic flow according to strategic sensors SDV1 and SDV5, which means: if at point SDV1 there is degree 2 and at the same time at point SDV5 there is degree 4, then you should select signal program number 6.=2 &SDV5=4 THENSP6

If the network does not classify the state of the traffic flow, then only one parameter is used for description, which is traffic intensity. Vehicle-dependent control is used in real time and receives signals from selected sensors every second. However, switching of signaling programs is carried out with a certain hysteresis to ensure stability in the transport network. In practice, this means changing the control device program over a period of several tens of minutes.

Offline optimization makes it possible to calculate the main controlled quantities: cycle duration, phase sequence, time offset and duration of green signals for a historical database (data from previous years). These data are obtained through long-term measurements using transport detectors. Based on long-term recorded data, a statistical model is usually developed, which for traffic volumes usually makes it possible to determine typical working days and especially Saturdays and Sundays, as a result of which changes in variables are greatly limited. An essential feature is that we are talking about macroscopic control in an autonomous mode, based on deterministic flow modeling and optimization algorithms, when signaling plan systems are calculated from a spatio-temporal vector of intensity data for previous years. Optimization models are used for offline calculations of signal time plans of transport control devices in a transport network or line.

In this case, the control process selects, depending on time, the most profitable of many pre-prepared signal plans. This method is called time-dependent control.

Advantages of time-dependent control:

possibility of simple control;

ease of modification of signal programs;

relatively low equipment and installation costs.

Disadvantages of time-dependent control:

it is impossible to improve the efficiency of using signal time (allowing movement for individual directions);

it is impossible to cover intensity peaks (a certain intensity reserve is required);

individual vehicles or pedestrians must not interfere with the control process;

the resulting traffic congestion cannot be eliminated.

3. Statement and analysis of modeling

The task of modeling traffic control strategies at a transport network node, as well as on the network, is to develop a classical fuzzy control module. Its components:

Fuzzification block: A fuzzy logic control system operates on fuzzy sets, so a specific value of the input signal of a fuzzy control module is subject to a fuzzification operation, as a result of which a fuzzy set will be associated with it.

The rule base is a set of fuzzy rules for determining the fuzzy set to which the output signal of the system belongs.

Solution generation block: direct determination of the membership set of the output signal for specific sets of input signals.

The defuzzification block represents a procedure for mapping the fuzzy set obtained at the output of the decision generation block into a specific value; it represents an impact control.

To build control strategies, it is proposed to use the TRANSYT software package, based on assessing the behavior of traffic flow using traffic modeling and allowing the selection of optimal parameters for the traffic light signaling operating mode. Based on the results of traffic simulation in the program, for various combinations of traffic intensity, the optimal time for a green traffic light signal is determined.

4. Development of a base of fuzzy rules for determining parameters for controlling the movement of traffic flows at a transport network node

Construction of a base of fuzzy rules for determining the optimal time of a green traffic light at an intersection characterized by maximum traffic volumes on intersecting roads. The necessary data was obtained using a transport detector.

We create a base of rules for classifying control strategies for a system with two inputs and one output:

1. Data is required in the form of a set. Next, we find the domains of definition of the elements of the set, which we divide into areas (segments), and the value of N is selected individually, and the segments can have the same or different lengths. Individual areas can be designated as follows: …, S,,…,.

We construct membership functions for a certain class of elements of a given set of training data. We propose to use triangular functions according to the principle: the vertex of the graph is located in the center of the partitioning area, the branches of the graph lie in the centers of neighboring areas. The degree to which data belongs to a certain class will be expressed by the value of the membership functions.

Then, for each pair, we define a rule for compliance with the control strategy class. The final rule for each pair of training data can be written down, that is

Since there are a large number of pairs available, there is a high probability that some of the rules will be contradictory. This refers to rules with the same premise (condition) but different means (conclusions).

One method of solving this problem is to assign so-called degrees of truth to each rule and then select contradictory rules for the one with the greatest degree of truth. After which the rule base is filled with high-quality information.

For example, according to the rules described above, the degrees of truth have the form

4. To determine the quantitative values ​​of the control strategy optimization parameter, it is necessary to perform a defuzzification operation. To calculate the output value of the impact control, it is possible and recommended to use the defuzzification method using the center of gravity method.

1 Construction of the membership function

For the elements of the set of training data for the system, we denote the following domain of definition

Dividing X 1 X 2 and G into 2n+1 segments and constructing membership functions of the form

Figure 4.1 General view of the graph of membership functions

As a result we have:

Figure 4.2 Graphs of intensity membership functions x 1 to classes of partition of the set X 1.

We determine the membership functions µ(x 1) on segments of the partition of the region X 1 by the method of assigning µ(x 1) to a certain class.

Table 4.1. Membership functions µ(x 1) on segments of the partition of the region X 1 (n=4)

	Split segment	Designation	Membership function µ(x 1)
			;
			;
			, ;
			, ;
			,;
			,;
			;
			;
			, ;

Figure 4.3 Graphs of intensity membership functions x 2 to classes of partition of the set X 2.

We determine the membership functions µ(x 2) on segments of the partition of the area X 2 by assigning µ(x 2) to a certain class according to Figure 4.3.

Table 4.2 Membership functions µ(x 2) on segments of the partition of the area X 2 (n=5)

	Split segment	Designation
			;
			,;
			, ;
			,;
			, ;
			,;
			;
			;
			,;
			;
			, ;

Figure 4.4 Graphs of intensity membership functions g to partition classes of the set Q.

We determine the membership functions µ(g) on ​​segments of the partition of the domain G by the method of assigning µ(g) to a certain class

Table 4.3 Membership functions µ(g) on ​​segments of the partition of the region G(n=6)

	Split segment	Designation	Membership function µ(x 2)
			;
			;
			;
			, ;
			;
			,;
			;
			,;
			,;
			;
			;

2 Construction of rules for compliance with a specific class of control parameter

We define a rule for compliance with the class of control strategies and assign a degree of truth to each rule.

Table 4.4 Values ​​of data membership functions for certain classes

(i)µ((i))(i)µ((i))g(i)µ(g (i))

We get a table with assigned degrees of truth and the degree of truth for each of the pairs x 1, x 2.

transport management road passenger

Table 4.5 Fuzzy rules generated from learning data and the degree of truth of these rules

3 Base of fuzzy rules

According to the rules defined in Table 4.7, we compose a base of fuzzy rules that determines the optimal value of the green traffic light signal.

Table 4.6 Base of fuzzy rules

Conclusion

In this work, the following issues were considered: the concept of adaptive traffic control at a transport network node, on the network, as well as a comparison of time-dependent and transport-dependent traffic control strategies.

Basic concepts of adaptive control, implemented in various countries and advantages such as: ensuring high performance in conditions of changing properties of the controlled object, environment and goals, through the development of new operating algorithms.

Organization of the movement of urban passenger public transport during the operation of an adaptive traffic control system, the implementation of this condition occurs through the installation of radio tags on vehicles and reading devices on traffic light objects. Vehicle recognition will make it possible to “stretch” the green light time and ensure unimpeded passage of public transport. You can also use the principle of data exchange directly between controllers of neighboring intersections. Data from detectors connected to the traffic controller is supplemented by data from those detectors installed at neighboring intersections. This allows you to prescriptively set the state of signal groups, and also ensures priority for public transport

Since adaptive control is very expensive, an alternative method was proposed to determine the optimal time for the green traffic light to remain on at an intersection. Namely, a method for developing a classical fuzzy control module, the initial data for which was a set of data on the intensity of two intersecting roads. In this work, the first three blocks of this method were considered and calculations were carried out.

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