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adaptive traffic control based on a system of microscopic simulation of traffic flows

A. S. Golubkov,

engineer, junior researcher

B. A. Tsarev,

cand. tech. Sci., Associate Professor Institute of Management and Information Technology Cherepovets Branch of St. Petersburg State Polytechnic University

The composition and features of the functioning 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 the system of microscopic simulation of traffic flows used in the system of adaptive traffic control are presented.

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

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 (SRNs) is far from being fully used. Increasing the traffic capacity of the road network can be achieved through the introduction of automated traffic control systems (ATMS). With the introduction of ASUDD, the following indicators are improved: the travel time of vehicles (TC) 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%; improves road safety.

Modern ASUDD

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

1) transport detectors (DT), which provide detection of vehicles and counting their number when driving along the lanes;

2) one or more computers for data processing with DT and calculation of optimal control signals;

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

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

5) means of communication and telecommunications used to combine the ASUDD software and hardware into a single system.

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

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

1. A control method using libraries, characterized by pre-calculation of a plurality of coordination plans and switching them based on the current averaged readings of strategic DTs by selecting the appropriate appropriate plan from the library.

2. The actual control method, characterized by the preliminary calculation of traffic light coordination plans, their switching according to the calendar schedule and the implementation of changes in these plans in accordance with traffic requests recorded by local detectors in certain directions.

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

Optimization of traffic flow management in modern ATCS is carried out by various methods. The Balance system (Germany) uses genetic optimization algorithms. In the Utopia system (Netherlands), the calculation is based on a price function that takes into account the delay time, the number of stops, specific priority requirements, and the relative position of intersections. In the Spektr system (St. Petersburg, Russia)

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

Adaptive Traffic Flow Management Based on Intersection Optimization Models

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

■ Diagram of adaptive traffic control system

control kts, the number of which corresponds to the number of controlled intersections in the system. All local points have a connection via communication channels with the central control point.

The central control point 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 intensities of traffic flows;

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;

The current lengths of the phases of traffic light objects at UDS intersections.

The local points in the system perform direct traffic management optimization at the respective intersections. Each local control center includes:

Transport detectors;

A computer that performs data preprocessing with DT, if necessary, and optimization of traffic control;

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 cameras enters the computer of the local control center, where the pre-processing software module performs video image analysis and estimates of traffic flow intensities in all controlled lanes. Further, the intensity of traffic flows are transmitted to the central control point.

Optimization of traffic control is performed 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 usually 2-5 minutes), the following actions are performed.

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

The optimization module launches runs of the intersection model with a duration of 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 the traffic light object are a vector of optimization parameters j = (fr f2, f3, f4) (no more than four phases are usually set at a crossroads). As the objective function F(j) can serve as the average waiting time for the passage of the intersection of the vehicle. In this case, the optimization criterion will be the minimum average waiting time for a passage

min .P(f) = F(^*),

where Ф is an admissible set of values ​​of the coordinates of the vector of phase lengths; j* - vector of optimal values ​​of phase lengths. The admissible set of coordinate values ​​of the phase length vector 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.

The calculation of the derivatives of the objective function on the model is impossible, therefore, only direct methods can be used as optimization methods. The use of alternating 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 length of the phase length variation step can be set to 2-3 s.

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

System for microscopic simulation of traffic flows

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

due to which the system has a high speed.

The performance of the system was evaluated 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 (actually, only one core was used in the experiments, since the simulation is performed in one program thread). As a result, simulation of traffic flows through a single intersection for 45 days (3,888,000 s) took 2864 s of CPU time. Thus, the excess of the simulation speed over the real time flow rate was 3 888 000/2864 « » 1358 times, i.e., during the real phase cycle at the intersection, the optimization module is able to perform more than 1300 runs of the optimization experiment.

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

On the contrary, in the system-dynamic approach, when the simulation is accelerated by increasing the sampling time 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 (Great Britain), TransModeler (USA), VISSIM (Germany) . In all the above simulation systems, a time sampling step of 0.1-1.0 s is used.

In a system-dynamic road and transport model, a simulation step in time equal to 1 s is quite capable of depriving the model of adequacy. Thus, a vehicle at a speed of 60 km/h travels more than 16 m in 1 s, i.e., at typical speeds, a 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 the urban environment. 2007. No. 26. S. 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 the model vehicles in the 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 taking into account traffic flows between neighboring intersections with an accuracy of individual vehicles. If there is a high density traffic flow in the road network in any direction, the control is automatically adjusted at all adjacent intersections with the organization of a green wave in this direction. At the same time, all other directions with traffic flows of lower density are also subject to optimization.

Optimization of the control of each individual intersection in real time is possible due to the use of a system of microscopic discrete-event modeling of traffic flows in the road network developed by the authors of the article. This modeling system, due to the use of a discrete-event approach, 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 management optimization is highly dependent on the accuracy of traffic density prediction. In this case, the prediction accuracy is the higher, the smaller the prediction time interval. When using sufficient performance hardware at local intersections, recalculation of the optimal lengths of the phases of the traffic light object regulation cycle can be performed with the beginning of each next 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, which will increase the optimization efficiency.

2. Brodsky G. S., Rykunov V. V. Let's go! ASUDD - world experience and economic sense // Mir roads. 2008. No. 32. P.36-39.

3. GNPO AGAT. http://www.agat.by (date of access:

4. Crowdhury M. A., Sadek A. Fundamentals of Intelligent Transportation System planning. - Boston - London: Artech House, 2005. - 190 p.

5. Kremenets Yu. A., Pechersky M. P., Afanasiev M. B. Technical means of organizing traffic. - M.: Akademkniga, 2005. - 279 p.

6. GEVAS software: Traffic Control. http://www.gevas.eu/index.php?id=149&L=1 (accessed 16.06.2010).

7. UTOPIA - Peek Traffic. http://www.peektraffic.nl/ page/484 (date of access: 16.06.2010).

8. CJSC "RIPAS": Development and production of automated systems. http://www.ripas.ru (date of access: 06/16/2010).

9. ASUDD - OJSC Electromechanics. http://www. elmeh.ru/catalog/3/asud (date of access:

10. Karpov Yu. G. Imitation modeling of systems. Introduction to modeling with AnyLogic 5. - St. Petersburg: BHV-Petersburg, 2006. - 400 p.

11. Sovetov B. Ya., Yakovlev S. A. Modeling systems. - M.: Higher. school, 2001. - 343 p.

12. Nagel K. High-speed microsimulations of traffic flow. Thesis: University Cologne, 1995. - 202 p.

13. Aimsun. The integrated transport modeling software. http://www.aimsun.com (accessed:

14. Quadstone Paramics. Traffic Simulation Solutions. http://www.paramics-online.com (accessed:

15. SATURN Software Web Site. https://saturnsoftware.com co.uk (Accessed 20 May 2010).

16. TransModeler Traffic Simulation Software. http://www.caliper.com/transmodeler/ (accessed:

17. PTV Vision - transport planning. http://www.ptv-vision.ru (date of access: 05/20/2010).

18. Company "Mullen". http://www.mallenom.ru (date of access: 20.05.2010).

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A modern automated traffic control system includes a combination of various technical means and software methods, the main purpose of which is to ensure the safe movement of vehicles and pedestrians (road users). An integrated professional approach to traffic management helps to reduce the number of accidents and prevent congestion, which leads to a significant improvement in the environmental situation in large cities. A carefully designed ASUDD system that meets all standards, combined with a well-designed traffic organization project, is a guarantee of safety on highways with a busy traffic flow.

If you understand the ASUDD system deeper, then this is artificial intelligence, sharpened for transport management, taking into account various factors, a specific object and a section of the road network. The ASUDD system is a part of the intelligent transport system (ITS). The ASUDD system adapts to the traffic intensity, performs an analysis and assessment of the situation, and then takes measures to unload the problem nodes of the road network.

The ASUDD system redistributes traffic flows by means of peripheral equipment, such as information display boards - TI (information dynamic boards), controlled road signs (UDZ).

By means of controlled road signs (UDZ), the ASUDD system redirects traffic flows to exits and transport hubs of less congestion or reduces the flow speed to prevent congestion at the exit. In case of a traffic accident, the ASUDD system can prohibit access to this section, thereby preventing the formation of a dead traffic jam in which road users would have to stay until the elimination of the consequences of an accident.

The information display panel is used to inform vehicle drivers about possible traffic jams and congestion in certain sections of the road network. I take into account the information received from the information board, the driver chooses ways to bypass the problem area of ​​the road network (UDS).

The collection of information for the analysis of the traffic situation is also carried out by means of peripheral equipment, such as vehicle detectors and surveillance cameras.

The automated traffic control system can also include traffic light objects, both at intersections, junctions, and reverse traffic lights. The interaction of all the listed equipment and the analytics and traffic control system is the automated traffic control system (ATCS). Such systems can be applied both globally (management of the entire city) and locally (management of a specific transport hub or section of the road network). The control system may include weather stations to assess weather conditions and to warn drivers about crosswinds, ice, snowfall and other elements.

Very often, the implementation of the ASUDD system is not complete without the design of supporting structures for the ASUDD equipment (information panels, controlled road signs), as a rule, these are support metal structures of U-shaped, W-shaped and L-shaped designs.

It is impossible to operate the ASUDD system without creating a communication line for the interaction of peripheral equipment and without creating cable lines for powering the equipment.

Also, when developing ASUDD systems, transport modeling is often used, which makes it possible to visually check the feasibility of installing the system even at the time of its inception, using computer technology.

Various types of ASUDD systems are used throughout Russia both in the urban environment and on the countryside - Federal highways and large industrial areas.

The need to create an ASUDD system

In the conditions of today's rapidly growing traffic of vehicles, the use and creation of an ASUDD system is necessary wherever there are traffic flows. This is necessary both to regulate traffic flows and to collect analytical and statistical data in order to create new ways to bypass problem areas in the future (creation of road transport infrastructure) - the creation of new roads and ramps, which helps prevent the formation of congestion with a constant increase in the number of vehicles.

We provide the following design and construction services:

  • New automated traffic control systems (ASUDD);
  • Modernization and reconstruction of existing ASUDD systems;
  • Temporary ASUDD systems;
  • ASUDD systems in industrial areas;
  • Autonomous ASUDD systems;
  • Integration of the ASUDD system into the intelligent transport system (ITS);
  • Construction of ASUDD systems of any type and complexity.

Each automated traffic control system, designed and implemented by our specialists, is a unique object, for the implementation of which it is necessary to carry out extremely accurate calculations, analyze the traffic situation and search for the most successful technical solutions. What goals are achieved during the active implementation of such a system?

  • the delay time of road transport at intersections is minimized, the number of forced stops in traffic jams is reduced, and fuel costs are also reduced;
  • the average speed of the traffic flow and the capacity of the urban transport network are increasing;
  • ensuring safety for all road users.

Installation of ASUDD is a modern method of dealing with congestion, traffic accidents and other negative consequences of an increase in the number of cars on the roads of megacities. The experience and practical skills of PRIMECAD specialists allow us to design and install a system of any complexity, as well as carry out its maintenance or modernization in full accordance with customer requirements.

Advantages of our ASUDD

  • Adaptability to the road situation. Due to the high level of automation, ASUDD is able to adapt to a specific urban environment - to regulate the time of operation of traffic lights, determine the optimal directions of movement, etc.
  • Possibility of rapid modernization. The system is characterized by sufficient flexibility, which allows changing the set of its components in accordance with current requirements.
  • Compliance with modern safety requirements. The equipment is controlled remotely using high-performance software systems that exclude the influence of the human factor.

Urban problems such as traffic jams can be dealt with in a conservative way, that is, a physical increase in road capacity, or in a “smart” way. In this case, all transport and people are combined into an ecosystem, and the city itself “makes a decision” how to distribute traffic flows. About our vision of such an ecosystem, we told at one of the Open Innovations forums. And in this article, we will discuss exactly how smart traffic management systems work and why they are so important to all of us.

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 it will be possible to do anything 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 facilitate the transition to the use of unmanned vehicles.

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.

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 increased 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. The system also 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.