Basic concepts of traffic management. Design and construction of automated traffic control systems (asudd)

23.07.2023

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Introduction

The growth in the number of cars, and as a result, the increase in their number on the roads of large cities is becoming an increasingly important problem today. A large accumulation of centers of gravity of the human masses in the center of most megacities leads to the complication of the management of the UDS and the rise in the cost of its maintenance. Many cities in the world cannot cope with daily transport challenges and are stuck in traffic jams for many kilometers every day.

At the same time, the demand of the population for transportation continues to grow. Consequently, without proper measures, the situation is moving towards a dead end. UDS designed for a smaller load can not cope and require modernization and optimization. Today, the city needs not only good, competently modeled, and then built roads, but also their high-quality management. Also, in many ways, the old methods of traffic control are becoming obsolete and do not keep pace with the growing city, and the multidirectional flow requires dynamic management and the integration of innovative systems to improve the traffic situation, and in particular in Moscow. The entire system of construction of the UDS and its management needs to be changed through new technologies, including mathematical modeling, which makes it possible to predict the behavior of the UDS, make adjustments to its configuration, and much more. That is why there is a sharp increase in the need for alternative, as well as any additional sources of information about the state of traffic. The latest complexes and systems for collecting and processing data are already being introduced.

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, an analysis of the usefulness of such data and the possibility of their use. At the end of the chapter, theoretical information is given 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, the selection of the “experimental” section of the UDS was made, its main problems were considered using the Yandex.Traffic heat map, and based on the problem statement, measures were proposed to improve the traffic situation at this section of the UDS.

The third chapter provides a detailed rationale for the proposed changes using computer simulation and comparison of two models of the CDS, and their parameters. A computer model was created on the basis of a real selected area, 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 of the two models was carried out, which allows 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 road network of cities.

The subject of the study is the possibility of using computer simulation 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 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 its computer model, compare the behavior of the model with the picture in practice, make improvements and changes to the structure of the UDS and further model the modified UDS in order to confirm the improvement of the situation in this area.

The reliability of the results of the studies carried out in the work is ensured by the experimental confirmation of the main hypothesis, the consistency of the results of theoretical studies 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 studies.

1 Analysis of the current situation and statement of the problem

1.1 Justification of the relevance of the problem

It's no secret that many major metropolitan areas of the world are experiencing huge problems in the transport sector. Transport in the metropolis plays a huge connecting role, which is why the transport system of the 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 the competent management of the transport system of the metropolis plays a huge role in its activities.

Every day the need of the population for transport provision is growing, both with the help of public transport and private cars. It is logical to assume that with an increase in the number of transport in the metropolis, the number of roads, interchanges and parking lots should proportionally increase, however, the development of the street-road transport network (SDN) does not keep pace with the pace of motorization.

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

car traffic flow computer

Figure 1.1 Number of cars per 1,000 people in Moscow

At the same time, the UDS of Moscow is not ready for such a growth rate of motorization in the city. In addition to personal transport in the city, the problem of public transport and passenger traffic in Moscow should be solved. According to the state transport program, only 26% of passenger traffic is accounted for by personal transport and 74% by public transport. At the same time, the total annual volume of traffic in 2011 was 7.35 billion passengers, and according to forecasts, it will grow, and in 2016 it will amount to 9.8 billion passengers per year. At the same time, it is planned that only 20% of this number of passengers will use personal transport. At the same time, in total, personal and elevated public transport account for more than half of passenger traffic in Moscow. This means that solving the problems of road transport in the metropolis plays a big role for its normal functioning and comfortable living for 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 intracity movement of passengers, the problem of transport flows of commuting labor migration, and the flow of vehicles (mainly trucks) 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 more than 12 tons during the daytime in the city, 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 gravity of the masses in the city. In particular, the location of a huge number of jobs 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 in the city is steadily declining in favor of their location outside the Moscow Ring Road). All this leads to the fact that huge human flows daily during the morning rush hour move from the region to the city limits and in the evening back to the region. This problem is especially acute on weekdays, when a huge number of people rush to work in the morning rush hour and go home in the evening. All this leads to a colossal load on the outbound highways used during these hours by a huge number of passengers traveling both by public transport and personal. In addition, in the summer, summer residents are added to them, every weekend creating huge traffic jams on highways to the region, and after the weekend they leave it.

All these problems require an immediate solution, through the construction of new roads and interchanges, the transfer of centers of attraction for the masses of people and the optimization of the management of the already existing UDS structure. All these solutions are simply not possible without careful planning and modeling. Since 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 estimate and the positive effect of influencing the UDS.

1.2 Analysis of the current traffic situation in Moscow using the Yandex Traffic jams web service

Considering in more detail the problems outlined above, we must turn to the 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 itself to be effective, is the Yandex Traffic Jam web service, which has proven to be effective and informative.

By analyzing the data provided by the service in the public domain, we can analyze the data and provide a factual justification for the problems outlined above. Thus, we can visually see areas with a tense traffic situation, visually consider trends in the formation of congestion and propose a solution to the problem by choosing 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. Thus, we can combine a theoretical model and a 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 traffic situation in Moscow and other cities of Russia and the world. By analyzing the information received, the service provides information about the traffic situation (and for large cities it also sets a “score” for the congestion of the transport network), allowing motorists to correctly plan a trip route and estimate the estimated 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 the optimization of the traffic flow, allowing drivers to choose detour routes that are not covered by traffic jams.

1.2.2 Data sources

For clarity, let's imagine that you and I are an accident on Strastnoy Boulevard in front of Petrovka (small and without casualties). With our appearance, we blocked, say, two rows of the existing three. The motorists who moved along our rows are forced to go around us, and the drivers moving along the third row have to let pass the bypassers. Some of these motorists are users of the Yandex.Maps and Yandex.Navigator applications, and their mobile devices send traffic data to Yandex.Traffic. As 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 app. For example, if an accident occurs on the road, then some conscious driver, having seen our accident, can warn other motorists about it by putting an appropriate dot in mobile Yandex.Maps.

1.2.3 Track processing technology

GPS-receivers allow errors in determining the coordinates, which makes it difficult to build a track. The error can "shift" the car a few meters in any direction, for example, on the sidewalk or the roof of a nearby building. The coordinates received from the users end up on the electronic map of the city, which very accurately displays all the 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 off” the corner. (Figure 1.2)

Figure 1.2 Track processing technology

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

After combining the tested tracks, the algorithm analyzes them and assigns "green", "yellow" and "red" marks to the corresponding road sections.

1.2.4 Combining data

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

Figure 1.3 Displaying traffic jams in Yandex.Maps

1.2.5 Scoring scale

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

The score scale is configured differently for each of the cities: the fact that in Moscow is a small difficulty, in another city is already a serious traffic jam. For example, in St. Petersburg, with six points, the driver will lose about the same amount of time as in Moscow with five. Points are calculated as follows. The streets of each city are pre-drawn routes, including the main highways and avenues. For each route there is a reference time for which it can be driven on a free road without violating the rules. After assessing the overall load of the city, the aggregator program calculates how much the real time differs from the reference one. Based on the difference in all routes, the load in points is calculated. (Figure 1.4)

Figure 1.4 Generalized scheme of the Yandex.Traffic portal operation

1.3 Using information obtained using the YandexProbka web service to find problem areas in the UDS

Summarizing the information received, we can conclude that the service provides very useful information (both online and in forecast mode) about the traffic situation in Moscow and other regions, which can be used for scientific purposes, in particular, to identify problematic zones, streets and highways, predicting congestion. Thus, we can identify primary problems both in the entire road network as a whole and in its individual sections, justify the existence of certain transport problems in the road network by analyzing the information obtained using this web service. Based on the data of primary analytics, we can build a primary picture of difficulties at the UDS. Then, using modeling tools and specific data, to confirm or refute the presence of a particular problem, and then try to build a mathematical model of the UDS with the changes made to it (change the phases of a traffic light, simulate a new interchange in a problem area, etc.) and offer a variant (s) improve the situation in a given area. Then choose the most suitable solution in terms of the ratio of efficiency and cost estimate.

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 DDD) in Moscow. Based on the information from the portal, we will try to assess the problem areas in the traffic control system of Moscow and offer systemic solutions to improve the traffic control system, as well as identify trends in the formation of congestion.

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

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

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

The vast majority of jobs in Moscow start 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 commuting labor migration (LTM), should fall on time intervals in the morning hours: from 6:00 a.m. (region - MKAD) to 10:00 a.m. (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 UDS

Figure 1.7 Presence of difficulties when approaching Moscow

Based on the analytics, at 7-00 we have difficulties at the entrance to the city on the main radical highways 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 score in the morning hours was reached at 9:56 Moscow time, traffic jams had shifted from the outskirts of the city to its center by this time.

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

Figure 1.11 TTK at 16-00

An improvement in the transport situation as a whole was observed until 15-40 Moscow time, the situation "to the center" did not worsen until the end of the day. The general situation tended to worsen from 16:00, while the situation began to improve at about 20:00 Moscow time. (Appendix A). On weekends, problems are practically not observed at the UDS, and according to the gradation of the Yandex.Traffic portal, the “score” did not exceed “3” for the entire time of daily observation. Thus, we can state with confidence the congestion of the city due to the concentration of centers of gravity of the human masses (jobs) in its center, and a much better picture on weekends when the problem of MTM is absent.

Drawing intermediate conclusions, we can say with confidence that the main focus of work should be to reduce the number of centers of gravity of the human masses in the city center and limit travel to this area, as well as increase 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 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 results obtained, 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 shifting to 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 management is vital, since road congestion is unidirectional. With the help of intelligent systems, we can change the capacity of the road in one direction or another (for example, using a reverse lane “turning it on” to the side with insufficient capacity), change and adjust the phases of traffic lights to achieve maximum capacity in difficult sections . Such systems and methods are becoming more widespread (for example, the reverse lane on Volgogradsky Prospekt). At the same time, it is impossible to "blindly" increase the capacity of problem areas, since we can simply move the congestion to the first place with insufficient capacity. That is, the solution of transport problems should be of a complex nature, and the modeling of problem areas should not take place in isolation from the entire road network system and be carried out in a comprehensive manner. Thus, one of the goals of our work should be 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 of September 28, 2009 N 767 approved the Rules for the classification of highways in the Russian Federation and their assignment to categories of roads.

Motor roads are divided into the following classes according to traffic conditions and access to them:

motorway;

High-speed highway

normal road (not express road).

1.5.2 Roads depending on the estimated traffic intensity

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

Table 2

	Estimated traffic intensity, reduced units / day.
IA (motorway)
IB (high-speed road)
Ordinary roads (non-fast roads)

		St. 2000 to 6000
		St. 200 to 2000

1.5.3 Main TP parameters 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 transport stream are:

flow rate?, flow rate l, flow density s.

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

The site is a segment of the road enclosed between two sections. The distance L, m between the sections is chosen in such a way as to ensure an acceptable accuracy of the velocity measurement. The time t is measured, from the passage of the section by the car - 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 in the area:

V = L / ?.

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

The following terms are used:

The average temporary speed V is the average speed of cars in the section.

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

Travel time is the time required for a vehicle to cover a unit length of a road.

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

Also, the speed of movement can be divided into:

Instantaneous Va - the speed fixed in separate 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 through the section of the road per unit of time. Uses shorter time intervals at high traffic volumes.

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

Additionally, the following terms are used:

Volume of traffic - the number of cars that crossed the section of the road in a given unit of time. Volume is measured by the number of cars.

Hourly volume of traffic - the number of cars passing through the section of the road during the hour.

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

Spatial interval or short interval lp, m - the distance between the front bumpers of two cars following one after another.

The average spatial interval lp.sr - the average value of the intervals lp on the site. The interval lp.sr is measured in meters per car.

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

1.5.4 Relationship between transport stream parameters

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

V?s

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

Figure 1.13 The relationship between the intensity, speed and density of TP: a) the dependence of the intensity of TP on speed; b) dependence of the TP density on the 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 applied. Conventionally, among the models, 3 classes can be distinguished:

· Predictive models

Simulation models

· Optimization models

Predictive models are used when the geometry and characteristics of the street network and the location of flow-forming objects in the city are known, and it is required to determine what traffic flows will be in this network. In detail, the road network load forecast includes the calculation of average traffic indicators, such as the volumes of inter-district movements, traffic intensity, distribution of passenger flows, etc. With the help of 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 evolution 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 in detail the movement will occur if “how much and where” is known. Thus, these two areas of transport modeling are complementary. It follows from the above that a wide range of models, known as traffic flow dynamics models, can be attributed to the class of simulation models in terms of their goals and tasks.

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

Flow forecast models and simulation models aim to reproduce 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, the problems of optimizing passenger transportation routes, developing the optimal configuration of the transport network, etc. are solved.

1.6.1 Dynamic traffic flow models

Most dynamic traffic flow models can be conditionally divided into 3 classes:

Macroscopic (hydrodynamic models)

Kinetic (gas-dynamic models)

microscopic models

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

Microscopic models are those models 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 the phase space. A special place in the class of micromodels is occupied by models of the cellular automaton type, due to the fact that in these models a highly simplified discrete in time and space description of the movement of cars is adopted, because of this, high computational efficiency of these models is achieved.

1.6.2 Macroscopic models

The first of the models based on the hydrodynamic analogy.

The main equation of this model is the continuity equation, which expresses 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 the road point with coordinate x at time t.

It is assumed that the average speed is a deterministic (decreasing) function of density:

Putting in (1) we obtain the following equation:

Formula 2

This equation describes the propagation of nonlinear kinematic waves with a transfer velocity

In reality, the density of cars, as a rule, does not change in jumps, but is a continuous function of coordinates and time. To eliminate jumps, a second-order term describing the 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 non-equilibrium situations that arise near road irregularities (exits and exits, narrowing), as well as in the conditions of the so-called "stop-and-go" traffic.

To describe non-equilibrium situations, instead of the deterministic relation (3), it was proposed to use a differential equation for modeling the average velocity dynamics.

A disadvantage of the Payne model is its stability against small perturbations for all density values.

Then the velocity equation with such a replacement takes the form:

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

The instability of a stationary homogeneous solution at density values exceeding the critical one makes it possible to effectively simulate the occurrence of phantom jams - stop-and-go modes in a homogeneous flow resulting from small disturbances.

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

1.6.3 Kinetic models

Unlike hydrodynamic models formulated in terms of density and average flow velocity, kinetic models are based on the description of the dynamics of the 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 velocity, velocity variation and other characteristics, which are determined by phase density moments in terms of velocities of various orders.

Let's denote the phase density as f (x, v, t). The usual (hydrodynamic) density c(x, t), the average velocity V (x, t), and the velocity variation H(x, t) are related to the moments of the phase density by the relations:

1) The differential equation describing the change in phase density with time is called the kinetic equation. For the first time, the kinetic equation for the traffic flow was 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 is understood as an event in which a faster car overtakes a slower car moving in front. The following simplifying assumptions are introduced:

· the opportunity for overtaking is found with some 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 the interaction;

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

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

· Only paired interactions are considered, simultaneous interactions of three or more cars are excluded.

1.7 Statement of the problem

In the course of the current study, we use static data on congestion using the Yandex.Traffic service as the main information. Analyzing the information received, we come to the conclusion that the UDN of the city of Moscow cannot cope with transport traffic. Difficulties identified at the stage of analysis of the obtained data allow us to conclude that most of the difficulties at the UDS take place exclusively on weekdays, and are directly related to the phenomenon of "MTM" (pendulum labor migration), since during the analysis of and holidays were not identified. Difficulties on weekdays bring the appearance of an avalanche flowing 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, 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 restrains congestion at a certain time interval, due to which the central part of the city travels in the optimal mode for some time. . Then, in the presence of all the same difficulties, traffic jams form in the MKAD-TTK zone, while traffic jams at the entrances continue to increase. This trend takes place all morning. At the same time, the opposite direction of movement is completely free. From this follows the conclusion that the traffic light management system and the direction of movement should be dynamic, changing its parameters to the current situation on the road.

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

However, this cannot be limited, since this “global congestion” does not have an end point. These actions should be put into practice only in conjunction with the restriction of 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 correctly 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 the city's roads during peak hours. Thus, all my theoretical assumptions are built with a “reserve for the future”, and the condition that the congestion will become final (the number of passenger flows to the center will decrease), the passenger flow will become more mobile (one bus with 110 passengers occupies 10-14 meters of the roadway, against 80 -90 units of personal transport, with a similar number of passengers occupying 400-450 meters). In a situation where the number of entrants 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 the road network in Moscow without investing large amounts of money and computing power, namely:

Use analytical and model data to identify problem areas

Development of ways to improve the UDS and its management in problem areas

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

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

Thus, on the radial highway, we will be able to understand the reason why it works 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. Creation of a model of a part of this radial highway in the place of the greatest difficulties.

3. Introducing improvements to this model based on analytics of the MAC using real data and simulation data, and creating a model with the changes made.

2 Creation of an improved version of the MAC

Based on the formulation of the problem and the analysis of transport difficulties in Moscow, to create a practical model, I chose a branch of one of the radial highways (Kashirskoye Shosse), on the section from the intersection of Andropov Prospekt and Kolomenskoye Proyezd to the Trade Center stop. The reason for the choice is many factors and in particular:

· Tendency to form congestion in the same places with the same trend

Vivid picture of "MTM" problems

· Availability of solvable points and the possibility of modeling traffic light regulation in this area.

Figure 1.14 Selected area

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

The presence of two problem points and their cross-influence

· Presence of problem points, changing of 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 in 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 the 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 movement during peak hours, here does not exceed 7-10 km / h: it takes about 30 minutes to travel a section of 4.5 km during peak hours. As for the duration, traffic jams to the center on Andropov Avenue start at 7 am and last until 13-14 pm, and traffic jams to the region usually start at 3 pm and last until 21-22 pm. That is, the duration of each of the "rush hours" on Andropov is 6-7 hours in each of the 2 directions - an exorbitant level even for Moscow, accustomed to traffic jams.

2.2 Two main reasons for traffic jams on Andropov Avenue

The first reason: the avenue is overloaded with unnecessary "over-run" traffic. From the metro station "Nakhimovsky Prospekt" to the center of the residential part of Pechatniki in a straight line 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 Nagatinskiy and Brateevskiy bridges there are 7 km in a straight line, and 14 km along the Moscow River. There are simply no other bridges and tunnels in this gap.

The second reason is 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 are left for traffic in each direction. Congestion is also greatly facilitated by 3 traffic lights (a transport one in front of Nagatinskaya Street and two pedestrian ones in the Nagatinskaya floodplain).

2.3 Strategic decisions on Andropov Avenue

To solve the problem of overruns, it is necessary to build 2-3 new links between the Nagatinskiy and Brateevskiy bridges. These transport links 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 many times faster (0.5-2 years) and 2 orders of magnitude cheaper (50-100 million rubles). Because it is possible to increase the capacity of the avenue by inexpensive local "tactical" measures in the most problematic places. This will meet the existing demand, improve all traffic indicators: reduce the length of traffic jams, reduce the duration of peak hours, and increase speed.

2.4 Tactical measures on Andropov Avenue: 4 groups

2.4.1 Step 1: Traffic light control

There are 3 traffic lights on the problematic section: two pedestrian ones in the Nagatinskaya floodplain and one transport one at the Andropov intersection with the street. Novelties and Nagatinskaya.

Two pedestrian traffic lights in the Nagatinskaya floodplain are already operating in the most “stretched” mode (150 seconds for vehicles, 25 for pedestrians). Additional lengthening of the cycle is unlikely to be effective for transport, but will increase the already considerable waiting for pedestrians. The only thing that can and should be done by traffic light regulation is to synchronize both pedestrian traffic lights so that vehicles spend less time on acceleration and deceleration. This will have little effect towards the center during the morning rush hour. Pedestrian traffic lights do not have a big effect on traffic in both directions at other times and towards the region in the evening. But with a traffic light at the intersection of Andropov with the street. New items and Nagatinskaya the situation is more interesting. It clearly keeps the flow towards the area during the evening rush hours. Further, the transport travels along the mass of alternative streets (Nagatinskaya Embankment, Novinki Street, Nagatinskaya Street, Kolomenskoye Proezd, Kashirskoye Highway and Proletarsky Prospekt).

Consider 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 traffic light operating mode

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

Secondly, the mode of operation of the traffic light from the time of day changes extremely insignificantly. 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 larger (people return home to Nagatinsky backwater).

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

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

Figure 2.4 Proposed time-based traffic light mode

As for the morning rush hour, it's pointless to "stretch" Andropov at this intersection in the morning to the center. The traffic does not use the entire length of the "green phase", because it cannot quickly pass the intersection due to traffic jams before the narrowing on the bridge from 4 lanes to 2.

2.4.2 Relayout

There are two problems with Andropov markup:

- dedicated lane on 3-lane sections of Andropov Avenue

- incorrect marking at the intersection with Nagatinskaya street and Novinki street

It's no secret that the dedicated lane has dramatically reduced the capacity of Andropov Avenue. This applies to movement both to the center and to the region. Moreover, the 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 1200 people per hour. This means that the allocated lane, contrary to its purpose, did not increase, but reduced the carrying capacity of Andropov Avenue.

I will add: the passenger traffic of land transport on Andropov Avenue has a chance to decline further. Indeed, already in 2014, it is planned 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 surface transport.

It would seem that to cancel the entire dedicated line for Andropov, and that's it. But analysis and long-term observations have shown that the dedicated lane on Andropov Avenue does not interfere everywhere, but only in those sections where there are 3 lanes (2 + A) in one direction and where this creates a “bottleneck”. In the same place where there are 4 lanes in one direction (3 + A), the dedicated lane does not interfere, and even allows you to increase the uniformity of traffic flows and performs the function of a lane for right turn, acceleration and deceleration.

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

towards the region on the Saikinsky overpass and Nagatinsky bridge, Saykina street

· towards the center on the entire section from the entrance to the Nagatinsky bridge to the Saikinsky overpass inclusive.

Figure 2.5 Locations where lane cancellation is required

Figure 2.6 Re-marking of Andropov Avenue

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

In addition to the dedicated lane, the poor marking of Andropov Avenue in the area of the intersection with Nagatinskaya Street and Novinki Street creates problems.

First, the width of the bands is large, and their number is insufficient. With this width of the roadway, it is easy to add a lane on each side.

Secondly, the marking, despite the widening of the crossroads, for some reason diverts all traffic to the left-turn lanes, from where those traveling straight have to “wade” to the right.

However, the ineptness of the designers is excusable: the knot is complex, the width of the carriageway "walks". This solution for this intersection also did not appear immediately. It allows you to increase the number of lanes in the area of intersections, and leave those driving straight in their lanes, “taking away” the direct course a little to the right. As a result, the number of lane changes will decrease, the speed of crossing the intersection will increase in both directions.

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

Figure 2.8 Proposed traffic pattern at the intersection

Local widenings

The next step is to carry out the now most necessary widening towards the center on the section from the Nagatinsky metro bridge to the exit to Trofimova Street. This would allow returning 3 lanes to private transport, giving the 4th lane to public transport - just like it was done in the direction of the region on this section.

Figure 2.9 Local widenings

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

Recently, the construction of an overhead crossing near the stop of the South River Station OT near the Nagatinsky metro bridge has begun. After its construction, the pedestrian traffic light will be dismantled.

Figure 2.10 Skywalk Construction Plan

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. Simultaneous construction of 2 crossings with the removal of both pedestrian traffic lights would have an excellent effect for the direction to the center: the throughput at the same width would increase by 30-35% due to the abolition of acceleration and deceleration in front of the traffic lights. But they are not going to build an off-street crossing opposite the Megapolis shopping center, which means that the second traffic light cannot be removed. And the effect of one elevated crossing will be insignificant - no more than from a simple synchronization of two traffic lights. Because in both cases acceleration-deceleration is preserved.

3 Rationale for proposed solutions

Based on analytics, we calculate problem points in a particular area of the UDS and, starting from 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 UDN, and after optimizing them, get the result of computer simulation, which can answer the question of whether the proposed changes will improve the throughput. Thus, using computer simulations, 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 Using computer simulation

Using computer simulation, we can, with a high degree of probability, predict the ongoing processes at the UDS. Thus, we can conduct a comparative analysis of the models. Simulate the current structure of the UDS with its features, modernize and improve it and create a new model, which will be based on the UDS with the adjustments made to it. Using the obtained data, we can get an answer at the stage of computer modeling whether it makes sense to make certain changes in the UDS, as well as use modeling to identify problem areas.

Why cities need a smart transport system

According to the 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, therefore, of course, they need to be eliminated.

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 after the main highway, and there will not be a single free lane near the repaired section during rush hour.

Of course, the authorities will try to build a forecast on which roads will quickly become congested. To do this, they will take into account the presence of traffic lights at intersections, the 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 anything will be done other than “manual control” of 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 acting as infrastructure elements. Vehicle location information enables real-time traffic redistribution, while additional systems such as smart traffic lights and parking areas provide efficient traffic management.

Reasonable Approach became the choice for a number of cities and proved to be effective. In Darmstadt, Germany, sensors help keep pedestrians safe and traffic free. They detect large groups of people about to cross the road and adapt traffic light phases to suit them. In addition, they determine if there is a stream of cars nearby, and "give the command" to switch the light only when the cars have finished moving.

And the traffic distribution system in the Danish city of Aarhus allowed not only to reduce traffic jams, but also to reduce overall fuel consumption. London's intelligent system notifies drivers of congestion in certain road sections. A "smart" traffic management system has helped Singapore become one of the least "busy" 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 real-time information flows, interprets them and makes an independent decision about traffic control (or helps a person in charge make such a decision). As a rule, a traffic control command center is formed around the platform.

Highways England photo /

A Geographic Information System (GIS) opens up the possibility of linking data to specific points on a road map. Separate subsystems serve for direct motion control. Their number, complexity and levels of interaction with each other may differ in different models depending on the tasks.

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

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

We propose to consider in more detail the structure of the most important subsystems of a "smart" city.

Intelligent monitoring and response system

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

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

"Smart" traffic lights

The principle of operation of this subsystem is simple: the so-called "adaptive" traffic lights use means to measure the volume of traffic, which signal the need for a phase change. When the 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 is complicated by a set of sensors that transmit data to the algorithms for analysis. In Tyler, Texas, this integrated traffic management solution from Siemens reduced traffic delays by 22%. Travel time on one of Bellevue, Washington's main thoroughfares has been reduced by 36% during rush hour since adaptive traffic lights were installed.

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 a car stream. This data serves as input to the system, which generates output signals for red, green and yellow phases and controls the cycle time based on the number of vehicles on each road.

The same information as an output signal can be transmitted to a road user. Adaptive traffic lights are also capable of operating in emergency mode, when video recording tools recognize a moving vehicle as an ambulance or a police car with signal beacons on. In this case, for cars that cross the route of the company car, the traffic lights will change to red.

Cameras that recognize the volume of traffic can also serve as sources of incoming data for the system. In the complex model of a “smart” city, information from cameras about the situation on the road is simultaneously transmitted to the software environment for algorithmic processing and to the 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 timing, efficient flow along the path, and report information to the next traffic lights. However, such a system remains decentralized, and each traffic light "makes its own decisions" on 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: it takes into account the current load on the transport system and the predicted unknown value responsible for the additional load that can enter the network at any time. Further, the algorithm is responsible for unloading the network at each node or, in other words, the intersection. Such a system, combined with artificial intelligence traffic lights, could be a solution to common urban problems.

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

"Smart" parking

The lack of parking spaces or their inefficient use is not just a domestic 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 finding free spaces and informing users.

As one solution to the problem, a Rice University team has developed a model that uses a camera that takes minute-by-minute photos to search for available seats. After that, they are analyzed using the 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 place (“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 received 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 a 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 LA Express Park Smart Parking Initiative is taking place in Los Angeles. Startup StreetLine, which is responsible for bringing the idea to life, uses machine learning methods to combine several 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 passed on to the decision makers. StreetLine provides SDK, automatic license plate recognition system and API to work with all data sources related to parking.

Intelligent parking systems can also be useful for managing traffic density. At the heart of such a decision is a tool for regulating traffic in advance - a change in tariff rates in paid parking zones. This allows you to distribute the load of parking spaces on certain days, thereby reducing traffic congestion.

For end users, data on free spaces and cheaper fares helps plan trips and enhances the overall driving experience - with wearable or in-vehicle devices, the user receives practical real-time guidance on how to find a parking space.

The future of motion control

The three main elements that we have considered are a ready-made ecosystem that can significantly alleviate 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 management systems are facilitating the transition to self-driving cars.

However, not everything is so simple here either: the infrastructure that is used in “smart” cities now may simply not be needed by drones. For example, if today it still makes sense to change the phases of a traffic light, 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 survive the global modernization after drones displace traditional cars from the roads, and we will see a new world without traffic lights, traffic cameras and speed bumps. However, so far a full transition to unmanned vehicles is unlikely. But the growth in the number of "smart" cities is a very real prospect.

WITHpiWithOKWithOToRAschenAndthAndObOhnAhenAndth, VWithTRechAYuschAndXWithIVTeToWithTe

ARM– automated workplace;

ACWITHAtD– aggregate system of traffic control facilities;

ACAtD– automated traffic control system;

ACAtD- WITH– PC-based automated control system;

INPAt– remote control panel;

GABOUTRABOUTD,GABOUTRABOUTD- M, GABOUTRABOUTD- M1 - names of automated traffic control systems using computers;

DC– road controller;

DBYAt– display control panel;

DP- control room;

DTP– traffic accident;

DTWITH– road transport network;

DT– transport detector;

DU– dispatching control;

ANDP– engineering console; ANDR– inductive loop; ANDC– center simulator;

KDA– control and diagnostic equipment;

TORC– controller of the regional center; TOTWITH- a set of technical means; KU– coordinated management; MnWITHX– mnemonic;

PTO– program of coordination;

PKU– control and management panel;

PEINM– personal electronic computer;

RAt- manual control;

WITHMEP– specialized installation and maintenance division;

WITHABOUT– traffic light object;

TVP- Pedestrian call board;

TE– transport unit (car);

TAND– telemetry;

TKP– scoreboard for collective use;

TP– traffic flow;

TWITH– telesignaling;

TSKU– telemechanical system of coordinated control;

TAt– telecontrol;

AtINTO– control computer complex;

AtDC– street and road network;

UZH– controlled road sign;

AtNOTP– a device for accumulating information on traffic flows;

AtP- control point;

AtWITHTO- indicator of the recommended speed;

CAtP- central control room.

1. Fundamentals of traffic management

1.1. Transport stream as a control object

The object of control of the ASUD is the traffic flow, which is described by a set of features that characterize the process of movement: intensity, speed, composition of the flow, intervals in the flow, and some other indicators.

The transport stream has quite definite properties that must be taken into account when choosing a control in the system. Therefore, we consider some of the most important features of the traffic flow.

1 . 1 . 1. WITHVOuchstVA TranssincetnOGO ByTOToA

Firstly, field surveys of vehicle traffic in cities show that the characteristics of traffic flows undergo significant changes during the day, arising from the uneven flow of cars into the transport network. This is the dynamic nature of the behavior of the control object.

Secondly, the daily periodic measurement of the same flow parameters at fixed time intervals of the day shows the statistical nature of the process of vehicle movement. The probabilistic behavior of the control object is due to the fact that the traffic flow is formed from individual traffic participants using different types of vehicles and having different trip goals (in time and space).

Thirdly, these statistical patterns of movement are stable due to the presence of deterministic trends in the movement of vehicles. Indeed, the vast majority of trips are periodic and often

is carried out on permanent routes (business trips, public routed transport, freight traffic). The collective behavior of the flow, which is the result of the interaction of participants with different goals and different psychophysiological characteristics, obeys the law of large numbers and makes the probabilistic characteristics of the movement of vehicles stable. It is the absence of chaos in the transport network that makes the functioning of the automated control system possible, which, in turn, contributes to even greater stabilization of traffic processes.

Fourth, the most important property of traffic flows, which largely determines the principles of management, is their inertia. Inertia is understood as a property of the control object continuously

move from state to state in time and space. Indeed, the movement parameters of transport units, measured at a certain point in time, cannot change significantly over a short period of time due to the fact that each unit has a finite, well-defined speed and can be detected in this interval within a limited section of the transport network. This property is manifested, first of all, in the fact that the average parameters of the flows (intensity, speed, density, intervals) change continuously in time and space. The presence of "packs" in the flows is also a result of the low variability of the structure of the flow during its passage through adjacent intersections, i.e. a consequence of inertia in changing the intervals between successive cars. The inertia of the control object indicates the possibility of predicting changes in its characteristics in small intervals.

Fifthly, all of the listed properties appear as a result of the interdependent movement of vehicles. This interdependence is expressed mainly in the fact that sometimes small changes in traffic conditions on individual highways and intersections (narrowing of the carriageway, changes in weather conditions, violation of traffic signaling) lead to a sharp change in the nature of traffic not only on this section, but also on distant highways. and crossroads of the city. The connectivity of regulated transport nodes is especially strong in network saturation modes, when traffic congestion that has arisen at a separate intersection extends to a significant section of the network. Network connectivity is complex and sometimes unpredictable. The stronger the connectivity property, the larger sections of the network must be considered when solving the control problem, and the more difficult this task, since the control object has to be understood not as individual intersections, but as all interconnected transport nodes.

The interdependence factor also manifests itself in the conditions of constrained movement of vehicles along the hauls and through the intersections of the network. In order to ensure the safe and fast movement of cars in the traffic flow, drivers are forced to perform various maneuvers due to the real traffic situation. As a result, the patterns of movement of individual vehicles can be considered as a consequence of the total interactions in the stream. The characteristics of the resulting interaction are those initial parameters for the system, according to which the issue of assigning a particular control is decided.

movement.

1 . 1 . 2. WITHOstoyaneitherI TranssincetnOGO ByTOToA

Let us dwell in more detail on typical cases of road traffic. Experimental and theoretical studies give grounds to single out three qualitatively different states, which we will agree to call WithVObOdnsm, GRatppoYoum And YouWellanddennsm .

At low traffic intensity, when the capacity of the road is not a factor limiting unhindered movement, the speed of vehicles is close to the speed of free movement. The interaction between transport units in the free movement mode is so small that it can be neglected. The state of a free transport flow is characterized not only by the independent movement of individual transport units, but also by the intervals between units in the flow that add up in this case. Numerous experimental works, as well as limit theorems

queuing say that the distribution of intervals in a free stream is close to exponential and, therefore, the number of arrivals of transport units of the stream in a certain interval in time or space is described by Poisson's law. The free state of the flow is observed in a real transport network on hauls with rare traffic in sections that are more than 800 m away from the supply intersections.

A different picture arises if we consider the group mode of motion. Group traffic of vehicles develops at slightly higher traffic intensities, when the capacity of the road and the intersection already has a significant impact on traffic conditions. In order to maintain speed, drivers of high-speed cars are forced to overtake, rebuild

and other maneuvers. In the free traffic mode, overtaking in the stream is carried out with little or no interaction between transport units. Group movement is characterized by the maximum interaction of units during movement, the maximum intensity of forced maneuvers. As a result, the entire traffic flow is divided into a set of queues having the speed of low-speed head cars. The speeds of high-speed transport units are falling at the same time. Now the movement of vehicles cannot be described by the Poisson law, since the distances between successive cars in queues are close to the safety distances, i.e. do not follow an exponential distribution. A typical example of a group flow is the movement of vehicles observed in the cross section of the span, located 20–30 m behind the intersection that feeds it. Bursts in the stream arising

after the passage of transport units through the intersection, as they move along the stage, they “fall apart” relatively slowly, and the flow in the section under consideration still has a pronounced group form.

When the traffic intensity increases and reaches the capacity of the road, the conditions for overtaking slow-moving cars by high-speed cars become more difficult, the queues formed during the group traffic mode lengthen and practically merge into a single queue. At the same time, the speeds of vehicles in the stream are aligned and turn out to be close to the speeds of the slowest cars, the intervals between transport units in the stream become close to deterministic, equal to the distances of safe movement. This mode of motion will be called forced.

Another feature of the control object is the presence of a development trend in it. Quantitative changes in the control object

associated with the natural growth of motorization, the construction of new regulated intersections, the construction of interchanges at different levels, the improvement of the dynamic characteristics of vehicles, the revision of the organization of traffic in the regulated area (the introduction and cancellation of turning movements, the introduction of one-way streets, the prohibition of passage on some streets for freight transport , prohibition and permission of parking, etc.). These quantitative changes lead, as a rule, to a change in the structure of flows, the degree of connectivity of individual intersections of the network, the scale of the regulated network, which may require a qualitative reconfiguration of the governing body and lead to a revision of the type of control algorithms for a particular intersection. Thus, the motion control system must necessarily be "flexible" in relation to the control object.

1 . 1 . 3. RAWithetcedelenie VRemennsX AndteRVAlov

Most researchers, considering the traffic flow on a section of the highway of considerable length, use composite distributions of the form to describe time intervals

F (d t ) =

A L- b 1 S +

B L- b 2 S

+ C L- b 3 S

where each of the three components of the description defines a certain part of the flow:

ü A L- b 1 S

ü B L- b 2 S

- freely moving;

– partly s t i ch n o s c o u n t i a n n a i;

ü CL- b 3 S is the associated part of the TP.

Each of the three coefficients A, IN, WITH means the proportion of traffic intensity that is in one of the three states, so their sum

Distribution (1.1) describes the TP quite well on highways of continuous motion. Considering the problem of describing the TP on urban

streets equipped with traffic lights, it is more appropriate to analyze

distribution of time intervals inside packs of cars as the regulated intersection moves away. This approach is closely related to the solution of the issue of the gradual disintegration of packs, and, consequently, the possibility of organizing coordinated traffic control.

Experiments by some researchers show that the normalized Erlang distribution is more suitable for describing time intervals within bursts.

F (d t ) =

l ( K + 1)

L l ( K + 1)d t . (1.2)

C a th e m a

With dispersion:

M k

D k =

1 . (1 . 3)

1 . (1 . 4)

l 2 ( K + 1)

This distribution is supported by the fact that, given different K, you can get any degree of consequence, therefore, reflect the degree of connectedness of the flow inside the pack. The effect of pack disintegration determines the dependence of the average traffic intensity inside the packs l and the order of distribution K from the distance of the pack to the exit intersection. Experimental studies have shown that a decrease in l and K as the pack moves away from the haul, it is well approximated by the exponential dependence

- H L

l n (L n ) = l + ( l n ace

L c ) L 1

n . (1.5)

K = [

K c + (K

on With

- K c

) L - H 2 L n

where l is the average traffic intensity along the entire stream;

l n A With –

intensity inside the pack at its exit from the intersection;

L n - distance

packs from the intersection;

K n A With – m a x i m a l p o r d o d

E. N G -d La Pa and Chk and, T O L E O TO ONE ON THE SHARTH ON THE SO PEL K R Yus TK A; K c

– order

E r l ang a s f o w e r t o r d i s t i o n s f o r f o n t m o u n t m o u n t

pack merging;

H 1 , H 2 – b u ck disintegration coefficient for

l n (l n )

And K ;

in square brackets is the integer part of the expression.

Experiments show that for a pack that has just left the intersection, the value K=9.

Practical research using ASUD in the cities: Kharkov, Minsk, Krasnoyarsk, Nizhny Novgorod, etc., conducted in

80 - 90 years, made it possible to obtain representative statistics on the traffic flow.

An analysis of the distribution of intervals at different intensities, as well as the minimum allowable intervals between cars, indicate the existence of three groups of cars in the traffic flow:

ücars moving freely, not affecting each other at intervals of more than 8 s;

üpartially connected cars moving at intervals of 1.5 -

8.0 s; the distribution of intervals is such that drivers of individual vehicles have the opportunity to maneuver within the stream;

ü connected part of the flow; in this case all the time

only small intervals of the order of 1.0 - 1.3 s are observed.

In practice, cars moving freely are observed at a rate of up to 300 cars per hour per lane. Partially tied cars are observed at a rate of about 300 - 600 cars per hour per lane. Bound traffic occurs at more than 600 vehicles per hour per lane.

One of the important tasks of the transport system is to ensure maximum efficiency in the management of the transport and road complex. To do this, it is necessary to use modern solutions, which include the means of displaying information. The article describes several projects where devices from Mitsubishi Electric were used to demonstrate traffic information.

The useful life of a traffic control center is on average at least 10 years. Obviously, during this time, ITS developers will inevitably face the problem of upgrading components that have exhausted their resources. But the existing infrastructure is not so easy to rebuild. Creating universal devices is a key approach that allows you to adapt to the changing rules of the game and the development of technology.

How can the principle of universality be implemented in information display systems used in control centers? One solution to this problem is a modular approach to hardware: the display is not considered as a single entity, but as a subsystem consisting of interchangeable components.

Currently, most modern control centers use rear projection DLP cubes, which are built on the basis of DMD technology (developed by Texas Instruments).

Following the principle of versatility, Mitsubishi has created a range of displays and related equipment that uses the latest technology based on a common architecture and the same set of components. In particular, the 70 and 120 series systems consist of DLP cubes and thin bezel LCDs in various sizes and configurations. As in the case of determining the configuration of a personal computer, the user, when ordering equipment, can specify the components that the system should consist of - with the possibility of upgrading it as needs change. An example is a projection unit. Two years ago, Mitsubishi Electric launched a new line of DLP projectors that make it possible to replace existing mercury-vapor video walls with the latest high-brightness LED systems. This technology improves image quality, significantly extends the life of existing systems and minimizes maintenance costs.

Mercury lamps have an average lifespan of 6,000 hours, less than one year of 24/7 operation. With an average lamp cost of €1,000, this entails significant operating costs. In contrast, Mitsubishi Electric Model 50PE78 LED Cubes have an expected lifespan of 100,000 hours, more than 10 years of continuous 24/7 operation. The use of LED cubes, combined with low-noise air-cooling fans, also rated for 100,000 hours of operation, virtually eliminates the need for routine maintenance of the display for most of its operating life. In addition, LED-backlit DLP cubes offer a wider color gamut and maintain a constant color temperature throughout their lifetime. This, in turn, means improved color reproduction and improved stability.

The project in Italy provides a good example of how engineers use versatile display system components to get around infrastructure constraints.

Autostrada del Brennero is the operator of the A22 motorway from Modena to the Brenner Pass (on the Italian-Austrian border). Considering the current analog display system in the control center to be outdated and too expensive to maintain, the company decided to upgrade it with the latest digital technology. The control system that existed at that time with 200 analog cameras and the software platform designed to control it were quite efficient. In addition, the company sought to avoid additional costs and the separation of operators from work in order to retrain them. 3P Technologies, a hardware and software integration company, has developed a solution that combines the latest display technologies with an existing control system and software platform.

The control room of the A22 motorway (fig. 1) is at the heart of a complex and high-tech traffic management system, which includes about 200 video surveillance cameras, monitors and emergency points connected by fiber optic cable, radio channels, and wired communication lines. The system is controlled by a specially designed software platform that, in the event of an accident, allows operators to control the input data or any information downloaded from the cameras. Also, the system has an innovative function of automatic recording of traffic events (AID), which makes it possible to analyze the data coming from cameras and sensors and automatically respond to emergency situations. In addition to the sound signal, the system records the incident and registers the events that happened shortly before it. This allows operators to restore the incident in dynamics.

Rice. 1. A22 motorway control tower

When developing the upgrade project, the main problem was the display used to control the system. Consisting of analog LCD screens, the display was not able to process the required type and volume of information, and was also expensive to operate. The existing system was replaced by a Mitsubishi Electric 70 Series LED Cube Video Wall, improving management quality, efficiency and reducing maintenance costs.

Used to drive the displays, Bilfinger-Mauell's X-Omnium processor provided versatility in how and where content was displayed. Whereas previously operators were limited in terms of display sizes, now they can organize the display of content in the form of windows anywhere on the screen. At the same time, the Crestron touch screen controller allows operators to call up ready-made scenarios using a simple touch interface developed by 3P Technologies.

Five Bilfinger-Mauell decoders provide an interface to an existing analog camera system, allowing operators to use familiar pan/tilt and zoom controls. It is important to note that the X-Omnium controller allows you to control the display itself using the available traffic control software package.

Another example of a project is the Senatra traffic monitoring center (Fig. 2), located in Andorra, in the Eastern Pyrenees region on the border with Spain and France.

Rice. 2. Traffic monitoring center "Senatra"

The Principality of Andorra is one of the most popular winter tourist destinations in Europe thanks to its numerous ski slopes. The high traffic flow (up to 27,000 vehicles per day) and the need for extreme vigilance due to winter conditions have made the center's display system and 60 network cameras vital to reliable security monitoring on the 100 km of main road and 150 km of secondary roads under its jurisdiction. center. DLP cubes from Mitsubishi Electric were also used for this.

Let's move on to another project. In 2015, Highways England expanded the capacity of the East Regional Control Center located in South Mimms. Among the seven regional centers of the company, the eastern one is one of the largest. It is responsible for managing traffic on some of the busiest roads in Europe, including the southern section of the M25 and a number of sections of the M40, M1 and M4.

The central place in the control room (Fig. 3), accommodating 20 equipped operator workstations, is occupied by a large video wall. From there, operators can view any of the 870 road network cameras, view video and data streams from other road agencies, and receive broadcasts directly from temporarily installed cameras.

Rice. 3. Control room of the Eastern Regional Traffic Control Center

The Eastern Regional Control Center operates 24/7. As part of the expansion of the center, a decision was made to modernize the video wall, and Electrosonic was chosen to implement the project. The main goal of the project, along with the installation of a higher performance display, was to introduce the latest technology in order to significantly reduce the cost of operating the video wall.

The implemented system is based on Mitsubishi Electric DLP video cubes model VS-67PE78 with a diagonal of 67″ in an 8×3 configuration. It allows you to increase the resolution of the main video wall from XGA to SXGA +, improve brightness and significantly increase the service life - up to 100,000 hours for LED light sources and other components.

The projects described show that any engineer designing a system should put the principle of universality at the forefront - especially in view of the coming revolution of machine-to-machine communication.

Traffic management is a set of measures aimed at the formation of optimal traffic modes.

Construction dictionary.

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