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Traffic control. Modern traffic control centers

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 activities include traffic control, which, as a rule, 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. A particular type of management is regulation, i.e. maintaining movement parameters within specified limits.

Taking into account the fact that regulation is only a special case of both control and organization of traffic, and the purpose of using technical means is to implement its scheme, the term “technical means of organizing traffic” or “technical means of traffic control” is used, which corresponds to accepted regulatory documents ( GOST 23457-86).

At the same time, due to established tradition, the term “regulation” has also become widespread. For example, in the Rules of the Road (SDA), 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 “control cycle”, “regulated direction”, etc. In the specialized literature, an intersection equipped with a traffic light is often called a “traffic light object”.

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 in the traffic rules, as well as by using a set of technical means and administrative actions of traffic police 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 (Fig. 1). Since some of the functions in the control loop are often performed by automatic equipment, the terms “automatic control” or “control systems” have developed. Control object.

Fig.1. Block diagram of the control loop.

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 Fig. 1, the feedback circuit that closes the automatic control loop is shown with a dashed line, taking into account that this connection may or may not exist. During manual control, feedback always exists (due to the operator’s visual assessment of driving conditions), therefore its circuit in Fig. 1 is shown as a solid line.

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, as a rule, through special communication channels from the 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 in the control center, i.e. away 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 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 time of movement of vehicles in a given area, is called coordinated control (control according to the “green wave” (GW) principle). In this case, system control is usually 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.

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Introduction

The growth in the number of cars, and as a consequence the increase in their number on the roads of large cities, is becoming an increasingly important problem today. 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.

A scientific hypothesis consists of the assumption of the possibility of using real data in a computer model, with its further (model) modernization, and obtaining improvement results that are highly likely to be 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, but the trend towards their construction within the city limits is steadily declining 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 the car's 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 end up on an electronic map of the city, on which all buildings, parks, streets with road markings and other city objects are very accurately displayed. 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 workload of the city, the aggregator program calculates how much the real time differs from the reference time. 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 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 problematic zones, streets and highways, congestion forecasting. 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 workers in Moscow begin work at 8-00 - 10-00 Moscow time, in accordance with the labor code, the working day for a five-day work week (the most common option) is 8 hours, so we can assume that the main load on the road network, in accordance with the hypothesis of pendulum labor migration (MLM), should fall on 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. The maximum level 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 the 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. Additional parameters characterizing the density of traffic flow are used.

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 “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 are 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 irregularities (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 model the dynamics of the 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

The instability of a stationary homogeneous solution at density values exceeding the critical one makes it possible 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 Statement of the problem

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 no holidays were 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 occurs throughout the morning. 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 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 unnecessary “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 allow traffic to be managed, 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.

Why do cities need a “smart” transport system?

According to WHO, more than 50 percent of the world's population lives in cities. Megacities mostly suffer from transport problems. Traffic jams are their most obvious and common manifestation. They negatively affect local economies and the quality of life of all road users, and therefore, of course, require elimination.

If, as an example, we consider a typical cause of traffic jams - repair work - conservative approach its solution will be to redirect traffic to the nearest parallel roads. As a result, most likely, they will be overloaded following the main highway, and there will not be a single free lane left near the area under repair during rush hour.

Of course, the authorities will try to make a forecast on which roads will become congested faster. To do this, they will take into account the presence of traffic lights at intersections, average traffic congestion and other static factors. However, at the moment when an 8-point traffic jam paralyzes the city center, it is unlikely that it will be possible to do anything other than “manually control” the situation, for example, by turning off traffic lights and urgently replacing them with a traffic controller.

There is another scenario for the development of the same plot. In a “smart” city, data comes not only from traditional sources, but also from sensors and devices both installed inside the cars themselves and as elements of the infrastructure. Vehicle location information allows traffic to be redistributed in real time, and additional systems such as smart traffic lights and parking enable efficient traffic management.

Reasonable approach has become the choice for a number of cities and has proven its effectiveness. In Darmstadt, Germany, sensors help ensure pedestrian safety and traffic congestion. They detect large groups of people about to cross the road and adapt the traffic light phases to accommodate them. In addition, they determine whether there is a flow of cars nearby, and “give the command” to switch the lights only when the cars finish moving.

And the traffic distribution system in the Danish city of Aarhus made it possible not only to reduce traffic jams, but also to reduce overall fuel consumption. London's smart system notifies drivers when certain road sections are congested. Smart traffic management has helped Singapore become one of the least congested major cities in the world.

What does a “smart” traffic control system consist of?

The key tool of a smart city is data. Therefore, the heart of the system is a platform that integrates all information flows arriving in real time, interprets them and makes an independent decision on traffic control (or helps the person in charge make such a decision). As a rule, a traffic control command center is formed around the platform.

Photo by Highways England /

A geographic information system (GIS) provides the ability to link data to specific points on a road map. Separate subsystems are used for direct motion control. Their number, complexity and levels of interaction with each other may differ in different models depending on the tasks assigned.

For example, in the Chinese Langfang the following subsystems operate: traffic light regulation, collection of traffic information, surveillance and warning, geolocation positioning of service vehicles and other components. In the Romanian Timisoara, in addition to the elements already described, subsystems for prioritizing public transport and recognizing license plates have been implemented.

The system of “smart” distribution of traffic flows can be complicated by various elements, but the main thing in it remains the platform, which manages all subsystems based on incoming data. From this point of view, cars are an important component of any model of a “smart” city. They are not only able to receive information (using devices such as WayRay Navion) and adapt to a specific traffic situation, but they themselves act as providers of meaningful information about road congestion.

We propose to take a closer look at the structure of the most important subsystems of a “smart” city.

Intelligent monitoring and response system

Monitoring is the basis of the command center. Timely detection of incidents and response to them guarantees road safety and reduced traffic jams. The user most often sees the monitoring results on a map with a color scheme that displays the traffic load in real time.

The data sources are cameras that automatically analyze the situation on the roads as cars move in their coverage area, as well as piezoelectric sensors. Another monitoring method in the smart city ecosystem is flow tracking based on a wireless signal, for example, from Bluetooth devices.

"Smart" traffic lights

The operating principle of this subsystem is simple: so-called “adaptive” traffic lights use means to measure the volume of traffic, which signal the need for a phase change. When traffic flow is difficult, the green phase of the traffic light for cars is active longer than usual. During peak periods, traffic lights at intersections synchronize their phases to provide “green lanes” for traffic.

In a “smart” city, the system becomes more complex due to a complex of sensors that transmit data to algorithms for analysis. In Tyler, Texas, this solution, as part of an integrated traffic management system from Siemens, reduced traffic delays by 22%. Travel times on one of Bellevue, Washington's major thoroughfares have been reduced by 36% during rush hour since the installation of adaptive traffic lights.

This is how this subsystem functions in its basic embodiment: infrared sensors installed in one of the elements of the road infrastructure, for example, in light poles, detect the occurrence or absence of traffic flow. This data serves as an input signal to the system, which generates output signals for the red, green and yellow phases and controls the cycle time based on the number of vehicles on each road.

The same information can be transmitted to the road user as an output signal. Adaptive traffic lights can also operate in emergency mode, when video recording equipment recognizes a moving vehicle as an ambulance or a police car with its warning lights turned on. In this case, for cars that cross the route of the official vehicle, the traffic lights will change to red.

Cameras that detect traffic volume can also serve as sources of input data for the system. In a comprehensive model of a “smart” city, information from cameras about the situation on the road is transmitted simultaneously to a software environment for algorithmic processing and to a control system, where it is visualized and displayed on screens in the command center.

There are also variations of “smart” traffic lights. For example, artificial intelligence technologies improve the coordination of traffic signals in a single ecosystem. In this case, the cycle is also triggered by sensors and cameras. AI algorithms use the received data to create cycle timings, efficiently move traffic along the trajectory, and report information to the next traffic lights. However, such a system remains decentralized, and each traffic light “makes its own decisions” regarding the duration of the phases.

Researchers at Nanyang Technological University this year introduced a traffic distribution algorithm based on machine learning. Routing in this case has several nuances: the current load on the transport system and the predicted unknown value, which is responsible for the additional load that can enter the network at any time, are taken into account. Next, the algorithm is responsible for relieving the network at each node or, in other words, intersection. Such a system, combined with artificial intelligence-powered traffic lights, could be a solution to common urban problems.

Smart traffic lights play an important role for drivers not only due to the obvious effect of reducing traffic jams, but also because of the feedback received by user devices such as WayRay Navion. For example, drivers in Tokyo receive signals from infrared sensors directly to their navigators, which build the optimal route based on this.

Smart parking

The lack of parking spaces or their inefficient use is not just a everyday problem, but a challenge for urban infrastructure and another reason for traffic congestion. According to Navigant Research, the number of smart parking spaces worldwide is expected to reach 1.1 million by 2026. They are distinguished from ordinary parking lots by automated systems for searching for free spaces and informing users.

As one solution to the problem, the Rice University team has developed a model that uses a camera that takes minute-by-minute photos to find available seats. Then they are analyzed using an object detection algorithm. However, within the smart city ecosystem, this solution is not optimal.

A “smart” parking system should not only know the status of each space (“occupied/free”), but also be able to direct the user to it. Devavrat Kulkarni, senior business analyst at IT company Maven Systems, suggests using a network of sensors for this.

The information obtained from them can be processed by an algorithm and presented to the end user through an application or other user interface. At the time of parking, the application saves information about the location of the vehicle, which makes it easier to find the car in the future. This solution can be called local, suitable, for example, for individual shopping centers.

Really large-scale projects in this area are being implemented right now in some US cities. The initiative to deploy a unified network of “smart” parking lots, LA Express Park, is being carried out in Los Angeles. The StreetLine startup, responsible for bringing the idea to life, uses machine learning methods to combine multiple data sources - sensors and surveillance cameras - into a single channel for transmitting information about the occupancy of parking spaces.

This data is considered in the context of the city-wide parking system and provided to decision makers. StreetLine provides an SDK, automatic license plate recognition system and API for working with all data sources related to parking.

Intelligent parking systems can also be useful for managing traffic density. This decision is based in advance on a traffic regulation tool - changing tariff rates in paid parking zones. This makes it possible to distribute the congestion of parking spaces on certain days, thereby reducing traffic congestion.

For end users, data on available spaces and cheaper rates helps to plan trips and improves the overall driving experience - through wearable or in-vehicle devices, the user receives practical guidance on finding a parking space in real time.

The future of motion control

The three main elements we have considered are a ready-made ecosystem that can significantly ease the situation on the roads of a modern city. However, the infrastructure of the future is created primarily for the transport of the future. Automated monitoring, parking and control systems are facilitating the transition to the use of self-driving cars.

However, not everything is so simple here: the infrastructure that is used in “smart” cities now may simply not be needed by drones. For example, if today there is still sense in changing the phases of traffic lights, then, according to researchers at the Massachusetts Institute of Technology, unmanned vehicles will not need the signals we are used to at all - the speed of vehicles and stopping at intersections will be automatically carried out using sensors.

It is likely that even the most advanced traffic management systems will undergo a global modernization after drones displace traditional cars from the roads, and we see a new world without traffic lights, traffic cameras and speed bumps. However, a complete transition to driverless cars is still unlikely. But the growth in the number of “smart” cities is a very real prospect.

UDC 517.977.56, 519.876.5

adaptive traffic control based on a microscopic modeling system for traffic flows

A. S. Golubkov,

engineer, junior researcher

V. A. Tsarev,

Ph.D. tech. Sciences, Associate Professor Institute of Management and Information Technologies Cherepovets Branch of St. Petersburg State Polytechnic University

The composition and functioning features of modern automated traffic control systems are described. A method for adaptive traffic control based on traffic flow prediction and fast intersection optimization models is proposed. The characteristics of a microscopic traffic flow modeling system used in an adaptive traffic control system are presented.

Keywords - adaptive traffic control, traffic control optimization, traffic flow modeling, microscopic modeling.

Introduction

Currently, in many large cities the problem of traffic congestion is very acute. At the same time, studies show that the potential of existing road networks (RSNs) is far from being fully used. Increasing the capacity of road networks can be achieved through the introduction of automated traffic control systems (ATCS). With the implementation of automated traffic control systems, the following indicators are improved: vehicle travel time is reduced by 10-15%; the number of general transport stops is reduced by 20-40%; fuel consumption is reduced by 5-15%, the amount of harmful emissions into the atmosphere is reduced by 5-15%; road safety is improved.

Modern automated traffic control systems

The main components of modern automated traffic control systems, in addition to traffic lights and traffic light controllers, are:

1) vehicle detectors (TD), providing detection of vehicles and counting their number when driving along lanes;

2) one or more computers for processing data from DT and calculating optimal control signals;

3) a set of software tools that implement algorithms for detecting transport and optimizing traffic flow management;

4) means of informing vehicle drivers (various information boards);

5) communications and telecommunications tools used to combine the software and hardware of the automated traffic control system into a single system.

Modern automated traffic control systems use various types of transport detectors: loop (induction); infrared active and passive; magnetic; acoustic; radar; video detectors; combined (ultrasonic, radar, infrared and video detectors in various combinations). All diesel engines have different efficiencies under different operating conditions. However, due to the achieved high level of development of computer and television technology, in many cases the most preferable are video detectors based on image processing and analysis technologies, as well as combinations of video detectors with detectors of other types.

In existing automated traffic control systems of various manufacturers, three main methods of adaptive traffic flow control are used in various combinations.

1. A control method using libraries, characterized by the preliminary calculation of multiple coordination plans and switching them based on the current average readings of strategic DTs by selecting the appropriate suitable plan from the library.

2. Method of actual control, characterized by preliminary calculation of traffic light coordination plans, switching them according to a calendar schedule and implementing changes in these plans in accordance with transport requests recorded by local detectors in individual directions.

3. Adaptive control method, characterized by constant recalculation of coordination plans and calendar modes based on information received from local and strategic (path) detectors in real time.

Optimization of traffic flow management in modern automated traffic control systems is carried out using various methods. The Balance system (Germany) uses genetic optimization algorithms. The Utopia system (Netherlands) calculates based on a price function that takes into account delay time, number of stops, specific priority requirements, and the relative position of intersections. The “Spectrum” system (St. Petersburg, Russia) uses

The following algorithms are used: search for breaks in the traffic flow; calculation using Webster's formula; switching programs according to intensity. The automated traffic control system produced by Elektromekhanika OJSC (Penza, Russia) uses the following algorithmic support: an algorithm for searching for a break in traffic flows; searching for a gap while maintaining the total duration of the coordination cycle; algorithm for switching pre-calculated modes based on traffic intensity control points; algorithm for dynamic recalculation of cycle parameters based on Webster's formula. The Agat automated traffic control system (Minsk, Belarus) uses the following heuristic control algorithms: selection of a coordination plan based on a time map; selection of phase, mode according to the coordination plan; selection of a coordination plan based on movement parameters at characteristic points, etc.

Adaptive traffic flow management based on intersection optimization models

The traffic control system being developed (figure) consists of one central point and many local points.

■ Diagram of the adaptive traffic control system

control units, the number of which corresponds to the number of controlled intersections in the system. All local points are connected via communication channels to the central control point.

The central control center performs the functions of collecting and processing information about the traffic intensity of vehicles in the road network. Information processing is the prediction of traffic flow values based on the following data:

Current traffic flow intensities;

Vehicle speeds;

Distances between adjacent controlled intersections in the system;

Prediction of vehicle routes based on statistics for the current day of the week and time of day;

Current phase lengths of traffic light objects at road traffic intersections.

Local points in the system directly optimize traffic flow management at the corresponding intersections. Each local control point includes:

Vehicle detectors;

A computer that performs preprocessing of data from traffic signals, if necessary, and optimization of traffic flow management;

A traffic light controller that allows external setting of the phase lengths of a traffic light object;

Traffic lights.

It is proposed to use video detectors as DT. In this case, the signal from the video cameras enters the computer of the local control point, where the preprocessing software module analyzes video images and assesses the intensity of traffic flows on all controlled lanes. Next, the intensity of traffic flows is transmitted to the central control point.

Optimization of traffic flow management is carried out as follows. The computer has an accurate software microscopic model of the intersection. When calculating the optimal phase lengths for the next phase cycle of controlling a traffic light object (the duration of the phase cycle is, as a rule, 2-5 minutes), the following actions are performed.

The model specifies the input intensities of traffic flows for the next 5 minutes (forecast of intensities from the central control point) with an accuracy of up to an individual vehicle.

The optimization module starts runs of the intersection model lasting 5 minutes of model time, for each run it sets new phase lengths of the model traffic light object

and calculates the value of the objective function based on the results of each run.

As a result of an optimization cycle consisting of several runs of the model, the optimization module finds the optimal phase lengths of the model traffic light object, corresponding to the extremum of the objective search function.

The phase lengths of a traffic light object are a vector of optimization parameters j = (fr f2, f3, f4) (at a cross-shaped intersection, no more than four phases are usually specified). The average waiting time for a vehicle to pass through an intersection can serve as the objective function F(j). The optimization criterion in this case will be the minimum average waiting time for travel

min .Р(ф) = F(^*),

where Ф is the permissible set of phase length vector coordinate values; j* - vector of optimal values of phase lengths. The permissible set of phase length vector coordinate values has the following form:

Ф = (ф|Tmin< Фi < Tmax.i = 1.-. 4} С r4.

where T. and - respectively the minimum

and the maximum value of the phase length.

Calculation of derivatives of the objective function on the model is impossible, therefore only direct methods can be used as optimization methods. The use of alternate cyclic variation of the phase lengths of a traffic light object from run to run with a constant step along the phase length is proposed. The step length for varying phase lengths can be set to 2-3 s.

A necessary condition for the possibility of implementing the described adaptive traffic control system is the presence of a microscopic modeling system of traffic flows, the operating speed of which would be sufficient to optimize the phase lengths of a traffic light object during one phase cycle.

Microscopic modeling system for traffic flows

The authors of the article have developed a system for microscopic modeling of traffic flows in road traffic systems, which can be used to optimize the management of traffic flows as part of an adaptive traffic control system. The main feature of the modeling system is the use of a discrete-event approach in modeling

thanks to which the system has high performance.

The performance of the system was assessed in a series of experiments with models of individual typical intersections. The experiments were performed on a computer with an Intel Core 2 Quad Q6600 processor with a frequency of each core of 2.4 GHz (in reality, only one core was used in the experiments, since the simulation is performed in one program thread). As a result, modeling traffic flows through a single intersection for 45 days (3,888,000 s) took 2864 s of processor time. Thus, the excess of the simulation speed over the speed of real time was 3,888,000/2864 " " 1358 times, i.e., during the real phase cycle at the intersection, the optimization module is capable of performing more than 1300 runs of the optimization experiment.

A feature of the discrete-event approach to modeling is the independence of the modeling results from the speed of model execution, i.e., even in full processor load mode, the modeling will show completely identical results to the results of execution, for example, in real time.

On the contrary, in the system dynamic approach, when accelerating the simulation by increasing the time sampling step, the accuracy of the simulation decreases. The system-dynamic approach implements the vast majority of modern systems for microscopic modeling of traffic flows: Aimsun (Spain), Paramics Modeler (Scotland), DRACULA (UK), TransModeler (USA), VISSIM (Germany). All of the listed modeling systems use a time sampling step of 0.1-1.0 s.

In a system-dynamic road transport model, a modeling time step of 1 s is quite capable of depriving the model of adequacy. Thus, a vehicle at a speed of 60 km/h covers more than 16 m of travel in 1 s, i.e., at typical speeds, the model vehicle is positioned only with an accuracy of about 10 m.

In the proposed discrete-event model, the positioning accuracy of model objects remains constant at almost any speed and depends on the bit depth used.

1. Brodsky G. S., Aivazov A. R. Automated traffic control in an urban environment // Road World. 2007. No. 26. P. 2-3.

variables and the type of arithmetic operations performed on them. When using double-precision floating-point numbers (64 bits, 15 significant decimal digits of the mantissa), the positioning accuracy of model vehicles in a discrete-event model at any time will be no more than 1 cm.

Conclusion

The proposed adaptive traffic control system is able to demonstrate high efficiency due to the exhaustive optimization of each individual intersection and the accounting of traffic flows between adjacent intersections with an accuracy of individual vehicles. If there is a high-density traffic flow in the road network in any direction, the control at all adjacent intersections is automatically adjusted to organize a green wave in this direction. At the same time, all other directions with traffic flows of lower density are also subject to optimization.

Optimizing the control of each individual intersection in real time is possible thanks to the use of a system of microscopic discrete-event modeling of traffic flows in road networks, developed by the authors of the article. Due to the use of a discrete-event approach, this modeling system has high performance and accuracy. In the near future, a trial version of the modeling system will be available on the developers' website.

The quality of traffic flow management optimization highly depends on the accuracy of traffic density prediction. In this case, the shorter the prediction time interval, the higher the prediction accuracy. When using hardware of sufficient performance at local intersections, recalculation of the optimal phase lengths of the control cycle of a traffic light object can be performed at the beginning of each subsequent phase. In this case, the actually used prediction time interval will be reduced to the duration of one phase, i.e., to 15-100 s, as a result of which the optimization efficiency will increase.

2. Brodsky G.S., Rykunov V.V. Let's go! Automatic traffic control system - world experience and economic meaning // World of Roads. 2008. No. 32. P.36-39.

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Introduction

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.

A scientific hypothesis consists of the assumption of the possibility of using real data in a computer model, with its further (model) modernization, and obtaining improvement results that are highly likely to be reliable and applicable in practice

1 Analysis of the current situation and problem statement

1.1 Justification of the relevance of the problem

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

automotive traffic flow computer

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

1.2.1 Brief information about the Yandex Traffic Jams web service

1.2.2 Data sources

1.2.3 Track processing technology

1.2.4 Data merging

1.2.5 Point scale

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

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

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.

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:

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):

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.

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.

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

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:

1.6.1 Dynamic traffic flow models

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

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:

1.6.3 Kinetic models

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:

1.7 Statement of the problem

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

2 Creation of an improved version of the UDS

2.1 Site analytics

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

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 allow traffic to be managed, stimulating not the “center-periphery” flow, but the “periphery-periphery” flow.

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.

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

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

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

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

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.