Moscow transport will switch to big data. Information technologies for managing urban passenger transport within the framework of the “smart city” concept Big data technology in transport

Moscow is a huge metropolis with 11,979,529 inhabitants, according to the 2013 census. Each of them goes to work, uses a mobile phone (or even more than one), goes down the subway, and gets stuck in traffic jams. All this is monitored by city services, government agencies, and private companies that provide various services. Thousands of video cameras, hundreds of thousands of sensors, monitors that control the life of the city, millions mobile phones, 3G/4G modems. And all together these are billions of data sources, by processing which you can obtain information for further planning the development of the city, managing its traffic flows, and ensuring the safety of the metropolis. One of the few tools that can cope with processing such a quantity of information are Big Data solutions. First, let's look at where they can be used.

Population density and population movement data

The main tool for determining the size and structure of the population, its distribution across the area this moment is the census. The main disadvantage of the census is the cost of conducting it and the lack of data on the movement of residents. The source of information for the census is the residents themselves, who are surveyed at their place of residence.

What benefits can the use of Big Data solutions provide? To answer this question, we first determine what data we need:

• where residents sleep and work;
• where they come from and where they go on weekdays and weekends;
• what kind of transport do Muscovites and guests of the capital use;
• where they come to the city from and why.

To collect this information, we first need to decide on the source of the data and the method for analyzing it. To determine the location of a resident, the most optimal way is to use data about his location cell phone(he is always with him). How to do it?

Available:

• data from mobile operators about the location of the telephone;
• data from specialized services (such as Yandex.Traffic);
• data from mobile applications with built-in location functionality provided by the city for the convenience of residents.

To analyze the received information, various algorithms can be used depending on the source, format, and method of their provision. But here are the main points.

Determining where residents sleep and work can be obtained by analyzing movement and activity data. For example, the periodic absence of calls from 22:00 to 7:00 and the absence of movement will show where a person lives, and the absence of movements during working hours will show where the same person works, and one of the criteria that increases accuracy will be the presence of activity on the subscriber’s telephone during given location. Here it will also be possible to determine how often a person moves during working hours, how many people in the city occupy positions related to constant movement (couriers, drivers and other professions).

Determining the direction of movement of residents is carried out in a similar way, using the same data on the movement of subscribers cellular communications, and allows you to highlight the main flows of movement of local residents, visitors, and labor migrants, collect movement statistics by area and destination, find out how often residents and guests visit shops, cultural events, city attractions, as well as how popular certain places in the city are.

By tracking the speed of movement and places visited, it is possible to identify what kind of transport a person uses: car, metro, ground public transport, intercity transport.

Analysis of the work of urban infrastructure and ensuring public safety

A large number of traffic lights, city traffic control systems, video recording systems (surveillance cameras), control of public transport within a city with a population of more than a million people requires a coordinated approach to data management and centralization. One of the problems identified at one time during the implementation of citywide video surveillance systems was the impossibility of monitoring ongoing events (for example, in order to identify illegal actions) by operational duty officers. Considering the current capabilities of modern technologies, it becomes possible creation unified distributed systems that provide both recognition of events from various sources (traffic control systems, surveillance cameras, etc.) and their analytics for the purpose of prompt response: calling the police, employees of repair organizations, etc. operational services cities. Another application of Big Data solutions is distributed and long-term storage of collected information, searching for necessary data and related events. What caused this or that change in the situation in the city, what events preceded it, who they affected - these are a small part of the questions that “big data” can answer.

Data Mapping

One of the key aspects of ongoing events is to determine the characteristics of the objects participating in them. Completely different sources can be used to collect data: for example, for data received from a cellular operator - characteristics individual, on which the SIM card is registered, for surveillance systems - information from facial recognition systems, departmental databases. One of the key points is the possibility of anonymizing information, excluding personal components when transferring data from various owners and sources.

Main problems

And yet there is a fly in the ointment in all this. The main problem of all integration solutions, especially if data exchange is carried out between different departments and organizations, are legislative restrictions that do not allow the provision of data in the form in which they exist. As a result, their preliminary processing is required on the owner’s side.

Total

To summarize, I would like to note that modern technologies for processing “big data” make it possible to provide the city with much more than existing IT services. In this case, there is no need to update the existing infrastructure, since the data sources that currently exist can be used.

With the help of Big Data class solutions, you can increase the convenience of city residents and its guests, reduce the number of traffic jams not due to restrictions on entry into the city, but by managing traffic flows, reduce the number of crimes due to prompt response, improve the quality of city services due to their prompt and automatic control.

Megafon has developed and presented to the subsidiaries of Russian Railways a test version of the service for analyzing passenger traffic, based on “big data,” RBC reports, citing the operator’s representative Maxim Motin. The tool helps determine the size and detailed characteristics transportation market, as well as the transport company’s share in it in near real time.

Now preparatory work is underway to implement a system for analyzing Big Data, confirmed Oleg Yemchenko, head of the ERP systems department (systems for enterprise resource planning) of the information technology department of the FPC Russian Railways. “This can only be translated into a specific project in 2016,” Yemchenko said.

Megafon launched the geoanalytics service back in 2013, the initial goal was to forecast network loads. With its help, you can estimate the exact volume of passenger traffic, obtain information about routes (who, when, where and where they are going), and a breakdown by mode of transport. The service also evaluates the solvency of passengers and the nature of travel (business trips, tourism, personal needs). All data is anonymized.

It is possible to analyze more than 10 thousand events per second using more than a thousand parameters, said Roman Postnikov, Megafon’s director for segment marketing and customer analytics. In three years, more than 5 petabytes of information have already been accumulated - a volume comparable to more than 30 billion photos on Facebook. Postnikov assures that each client has its own list of parameters for analysis, that is, in fact we're talking about about a universal cloud solution that can be used by completely different types of customers who need to analyze large amounts of data.

Megafon has calculated that transport companies in Russia spend more than 1.2 billion rubles annually on passenger flow research. “At the same time, companies themselves can collect only part of the data available to them, but our service makes it possible to see the whole picture of the market as a whole,” states Postnikov. Even if, thanks to the introduction of the service, the carrier is able to increase its share in the overall passenger transportation market by 1.5–2%, then this is billions of rubles, he says.

Big Data solutions can also be used to manage urban infrastructure. The Electronic State Expert Center, the Moscow government is going to conclude a contract under which the city will receive aggregated anonymized geospatial data of users of local telecom operators in 11 different sections over the course of two years. The consumers of this information will be the State Unitary Enterprise “Research and Design Institute of the General Plan of Moscow”, the Department of Transport and Development of Road Transport Infrastructure, the Department of Culture and other metropolitan departments.

The capital has been predicted for many years to experience a traffic collapse due to the rapid increase in the number of cars on its streets. However, the intelligent transport system being implemented in the city in recent years does not allow this forecast to come true. Alexander Polyakov, director of the research and design institute of urban transport in Moscow (SUE MosgortransNIIproekt), who since 2013 has been in charge of the development of transport analytics, building information systems and development, spoke about how traffic is managed in the capital. comprehensive programs development of transport infrastructure, being the deputy head of the Center for Traffic Management of the Moscow Government. At the BIG DATA 2017 forum, held by the Open Systems publishing house on March 29, he spoke about how the Moscow transport complex uses Big Data to develop an intelligent transport system, how traffic control systems are created on their basis, and how to solve For our tasks, we can use virtual and augmented reality tools.

- When did the “digitization” of Moscow transport begin?

It all started with a resolution on the development of an intelligent transport system in the city, which the Moscow Government approved on January 11, 2011.

Since then, the Department of Transport has been working to develop transport infrastructure using modern Information Systems.

As part of the project, in 2014, a situational center of the data center was created, whose specialists are responsible for organizing traffic and all the systems involved in the work of this center, including those that allow controlling traffic lights and television cameras, monitoring traffic conditions, visually informing road users, photo and video recording of violations of control of ground urban passenger transport.

- Which countries' projects were taken as samples?

The experience of European countries, in particular Spain and Germany, was taken into account; the experience of Singapore, Hong Kong, and a number of US cities was also taken into account. But at the same time, we understood that each city is unique, so Moscow’s transport infrastructure is developing according to its own scenario, not to mention the load on the streets. Now, let’s say, there are 683 thousand cars driving around Moscow.

- How is the traffic situation in the capital managed now?

In recent years, a number of IT systems have been created within the Moscow transport complex that solve various problems in this area, including using Big Data.

The static transport model, built in 2013, makes it possible to predict the situation for a long-term period, taking into account various options for changing the road situation. With its help, you can calculate scenarios on a city-wide scale, be it long-term traffic closures or the commissioning of new overpasses.

This model, among other things, takes into account data about residents provided to us by various services: about the number of people, their age, gender, social status, how many are working, how many are not working, etc. Moscow is divided into so-called transport areas, and we We analyze where residents of each such area go, why, and at what time.

Thanks to the data obtained, we analyze the correspondence matrix - the totality of all “exchanges” of traffic between districts. For example, if there are 600 preschoolers and 500 places in kindergartens in a district, then it is obvious that a hundred children will be taken to another district in the morning. To clarify the overall picture of what is happening, we conduct surveys that help us understand what type of transport and in what cases people choose: when - a personal car, when - public transport. In addition, we need to predict how people’s transport preferences will be affected by certain changes in urban planning or traffic patterns, what the consequences of closing a road during construction or, conversely, opening a new one.

We monitor the current situation using a dynamic transport model, which gives a complete picture of Moscow traffic in real time and allows us to respond to emerging problems. To do this, the DTM aggregates data obtained from GLONASS sensors installed on urban transport, photo and video cameras, transport detectors - radar sensors that read traffic intensity, vehicle speed and a number of other parameters.

DTM allows you to control traffic lights, analyze problem areas, for example, detect hotspots of accidents, places where congestion occurs all the time; identify difficulties in the movement of passenger transport and eliminate them; monitor the operation of mobile photo and video recording systems (the so-called parkons that record offenses), assess transport demand based on the daily correspondence matrix.

Based on the DTM, an interactive traffic map of Moscow was created, which displays real-time information about road congestion in points, the number of accidents, vehicles on this moment and per day, ground urban passenger transport, the number of traffic violations recorded by cameras.

In 2015, data center specialists based on a dynamic model created a virtual and augmented reality system that simulates a flight over the city and provides data on the traffic situation online. Thanks to this system, you can already see the resulting congestion by connecting to a camera that shows the real three-dimensional image this area, which allows you to better understand the situation.

For citizens, this map provides various information (text, photo and video) about significant historical, cultural and social objects, essentially augmented reality.

- Through what channels do you inform citizens about the road transport situation?

The data received from the DTM is broadcast in real time by a number of radio stations, Telegram messenger, road signs. The Moscow 24 TV channel and its Internet portal m24.ru display a map of the current situation on the city’s roads.

Such information is also a means of managing traffic flows. Muscovites see what the situation is like on the streets they are interested in, choose detours, consider the possibility of traveling by other types of transport, for example, switching from personal to public.

- Are there any numerical indicators of the effectiveness of your work?

A comprehensive traffic management scheme, designed to optimize the management of traffic flows on city streets, as well as increase their throughput, went into operation in 2015. And already in the first year we managed to achieve considerable results.

Let me give you these numbers. There are now 4.6 million cars registered in the city, and the accident rate, according to the traffic police, is the lowest in the last ten years. In 2016, compared to 2010, the number of road accidents decreased by 45%, and the number of deaths by 56%. In the central part of the city, inside the Third Transport Ring, the average speed of individual vehicles increased by 11%, and that of passenger transport by 7%. On dedicated lanes introduced in 2016, passenger traffic increased by an average of 11%. The average ambulance arrival time has decreased from 21 minutes to 8, almost three times, thanks to the fact that lanes for public transport have appeared, and buses and trolleybuses can give way to ambulances, going into “pockets” at stops.

If we compare more recent periods, then in 2016, compared to 2015, the number of accidents with property damage decreased by 18%, accidents with injuries decreased by 12%, and the number of collisions with pedestrians decreased by 14%.

- On the basis of whose solutions are the data processing center developments based?

We take the best Western developments. For example, the current traffic light control system is based on a Spanish solution, while the static transport model is built on a German platform. But the solution that combines all these developments is domestic. Our specialists integrated all these systems.

Based on the accumulated experience, we create solutions for managing traffic situations for other cities, both in our country and abroad. For example - for Tehran.

- Are we just catching up or are we already ahead of other countries in some ways?

We are on the way to a new management model. Last year at the base automated system traffic management launched a pilot project for automatic control of traffic lights. Now the system operates on Altufevskoye and Varshavskoye highways, as well as on Andropov Avenue, where traffic light operating modes are automatically changed based on traffic indicator data on highway congestion. There is no such thing in any city in the world. For example, even in the London transport management system Transport for London, the operating modes of traffic lights are accepted by operators.

Now we set ourselves the task of expanding the work of this system to other highways. The difficulty is that all the roads are interconnected, and while “clearing” some, it is necessary not to completely stall traffic on others.

- What new projects are planned?

We continue further development traffic accident forecasting systems. To carry out the forecast, it constantly analyzes weather conditions, the characteristics of problem road sections (configurations of bottlenecks, the degree of their reduction bandwidth), traffic flow indicators (average traffic congestion scores in the city and on the road section, flow speed on the road section, etc.).

We must be prepared for driverless vehicles in the future. Their navigators will already be loaded with information, for example, about the speed limit in a particular area, and the car will independently select a safe speed limit.

Long-term prospects include the development of a public transport system, which should become an attractive alternative to a personal car. Among other things, developed transport infrastructure is an important economic factor that contributes to the competition of cities in attracting tourists, entrepreneurs, etc.

Augmented reality systems will help relieve congestion on roads. If it is possible not to go to a conference, but to watch videos of presentations in 360° format from the workplace or even take part in it, not through special glasses, but on a smartphone screen, then many will prefer this option.

Moscow transport and traffic control in numbers

In the data center, located under the building of the situational center of the data center, more than 100 servers are installed, on which a total of about 2 PB of data is stored. Some information is constantly updated - for example, data received from cameras is stored on servers for seven days. Due to the constant growth of data flow, it is planned to significantly increase server capacity.

On an ordinary working morning, about 700 thousand cars leave for the main “transport arteries” of Moscow.

During rush hour, 71% of passenger traffic comes from public transport, so the Department of Transport puts its interests at the forefront.

Video recording cameras recognize up to 22 types of offenses - including driving on the side of the road or a dedicated lane, turning from the second row, entering a busy intersection, not allowing a pedestrian to pass, driving trucks without a permit, etc. During the day, they transmit information about 100 thousand to the traffic police. violations (rounded value).

There are concepts of “transport noon” and “transport midnight”. In Moscow they are shifted - “noon” lasts from 14:00 to 15:00, and “midnight” begins at 3 am.

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Speaker: Philip Katz

Interviewer: Alexey Karlinsky

Many times we believed the promises of science fiction writers about an incredible future, and each time our hopes were dashed by the dull present. We still live on earth and our cars don't fly through the air. “We have been deceived again!” we think, and behind all these fantasies we once again miss the moment when the future really comes.

This time it happened with the advent of Big Data. We can ignore them, but we can no longer deny their influence on our lives. Architect and Big Data specialist Philip Katz talks about how Big Data has quietly changed our cities and the way we live in them.

A multidisciplinary specialist and architect by training, Philip is a Big Data specialist. Graduate of the Kazan Architectural University, the Strelka Institute of Media, Architecture and Design, one of the founders of the Branching Point project. He teaches at the St. Petersburg National Research University of Information Technologies, Mechanics and Optics and is engaged in data analysis for the Rambler&Co company.

Close

Philip, please tell us how Big Data technologies are used in architectural design and urban planning today?

Let's start with the fact that four years ago, when I studied at Strelka, in Russia, at least, no one knew about Big Data. The world has just started talking about them. A year later, everyone in Russia already knew about them and got sick of them. It seems to me that this is very much a traditional dynamic - when new technology rises to the pedestal, is praised, and then quite quickly skepticism appears towards her. Technology is knocked off its pedestal and they are then integrated into society in a more relaxed manner.

If we talk about architectural or urban planning analytics, then, it seems to me, today this is a kind of compromise between modern technologies and traditional analysis. For example, a year ago I helped my friend participate in an architecture competition for students in the USA. For them, the city manager provided GIS files with quite good description data: transport routes, the volume of these routes, where puddles appear every year, where it floods every five years, where blocks with high level taxes where there are blocks with a high percentage of black residents. In the United States, the detail of statistics is high and the data is compiled quite well, so even at the level of a competitive project we were able to receive some things in finished form. They did not need to be collected or analyzed.

Most of the most useful analytics, in my opinion, boils down to this: you take some data as facts and design based on it. And although everyone may have the same data, they still read and understand it completely differently.

Google says its self-driving cars could reduce the number of car accidents and help make more efficient use of fuel and road space / photo: Google.com

How have you used Big Data technologies in your practice?

We for a long time We did the “Branching Point” project with my colleagues Edik Khaiman and Sasha Boldyreva - we tried to somehow discuss and develop digital design and, naturally, then our common postulated dream and ultimate goal was design based on parameters. At the same time, our ultimate dream was precisely to find, on the basis of some tricky code, new formal solutions that would meet our requirements, but the form of the result would not be the one we had in mind, but something unexpected - beautiful .

Analytics is an art form where, in each specific case, the algorithm for working with data is a picture

As the project matured, we all understood that this dream was not so much unattainable, but rather that the idea that a building should be designed entirely based on data alone was controversial. It's more like something you need to strive for, but understand that you will never get there.

Here an important dialectical moment arises for me. Let's say we are making an algorithm and understand that, first of all, due to genetic requirements, it requires quite simple, but still formal parameters. And in a complex system, and a building or area is a complex system, many parameters immediately appear that need to be brought to a single denominator. You always need a primary formal gesture, some form: a cylinder or a parallelepiped, pyramids, and so on.

If we look at the work of Zaha Hadid, there is always some graceful formal gesture at the heart of the project. It can then be modified digitally, but it always remains at the core of everything and belongs to the pen of the author. A genetic algorithm can then choose the best of the resulting options, but it will never be able to invent them.

That is, the basis of design will always be human will. How, then, will the degree of human involvement in design change with the development of Big Data?

In the future I see some kind of analytical engine - large and complex quantum computer, for example, or telepaths and parapsychologists immersed in deprivation chambers who predict something or suggest something to pay attention to.

I think a person will never be squeezed out of the process. All these things (Big Data analysis methods) are called decision aid algorithms, and their essence boils down to identifying anomalies in process dynamics as efficiently as possible and minimizing the percentage of technical labor per person. The analyst must be an expert in the field of working with them, and algorithms can bring him everything on a silver platter, except, in fact, the solution. Of course, there is a technical threshold for entering this discipline, but analytics itself is a form of art, where the algorithm for working with data is a picture. Masterpiece.

Drones equipped with a camera can independently patrol a given area and transfer images to an information center in real time / photo: Kevin Baird / Flickr.com

Big Data cannot cover all information. How to work with what is not taken into account when analyzing Big Data?

Indeed, analysts are often criticized for describing only those who are connected to the Internet, and those who are not connected to the Internet are excluded from the analysis. This is the absolute truth, but there is a logic of protection here. Cynically speaking, if we do not know the problem of a grandmother who is embarrassed to write on the Internet because she is not used to it, then we can ignore her problems, simply because if we use this approach, then either the grandmother or her grandson will take care of her, eventually they will write.

Another problem lies in the fact that any technology for collecting or storing data is always the first factor of error. At the same time, it is impossible in principle to track all the multifactorial factors - why people played this way and not otherwise. At first, Big Data does not provide an answer. They allow you to ask serious questions.

How does being able to ask questions change the way we think about a city?

Edward Hyman once coined the term "plagopolis". The idea is that the modern city is becoming more proactive and dynamic. Today it is a kind of environment with its own flows, movements, where the liquid that flows in the vessels constantly self-regulates. At the same time, you can only grab a point and fix it very conditionally. It will instantly change itself and change other points around itself. For me, this idea is a fairly practical thing to work with. It is now becoming clear that we can no longer perceive the city as something mechanical.

Is this idea accepted in Russian urban planning?

At the level of urban planning in this Russian understanding, this is not obvious. We, one way or another, start by drawing paths and streets, and we believe that this will be the case in the end. At best, we begin to think that we need to check how to do it correctly, and then it will either be the way we draw it, or people will then redo everything themselves.

Big Data doesn't provide the answer. They allow you to ask serious questions

In general, unfounded statements based on stereotypes and abstract ideas are very annoying today. Moreover, architects and urban planners are primarily driven crazy. They simply say that “pedestrians are better than motorists” or that “creative businesses will turn an industrial park into an earthly paradise.” I wish there was a basic calculation behind any of these things, because it may or may not be the case, and most of the time there is something wrong.

How then can Big Data help us better understand the city?

A city is always an elephant from a fairy tale about blind people who try to describe it by touch. We always work the same way - someone grabs the butt, someone grabs the ear, someone grabs the trunk. And everyone says that they see an elephant. In our case, we all also believe that we are sighted and know what a city is.

Big Data protects us from touching only in one place, gives us the opportunity to roughly imagine the general shape of the elephant and understand that we are touching approximately this place, but there are others. I receive huge reports on the city and I can always look at some specific ten lines of data, look and ask: why is this? Usually this becomes the beginning of some kind of investigation, research, history.

GIS data combined with spatial modeling algorithms help predict the level of insulation in a selected area / photo: Trevor Patt / Flickr.com

Do these thoughts, inspired by Big Data, somehow subsequently translate into real projects?

There is a so-called “urban acupuncture” method. Its essence lies in the fact that pain points are looked for in the city, and in these small nodes - in spaces of a maximum of a block, or better yet in one building, or even on some small area between buildings - some kind of change is made. Due to the size of the budget, it is completely microscopic, but the changes for the city as a whole, if these nodes are calculated correctly, are enormous.

Although “urban acupuncture” today is rather a speculative project; there are already smart spatial solutions, with traffic lights in unified system, For example. These, coupled with smart roads, allow space to change, and this can produce unexpected emissions. Robotization of industries is still taking place today, and this also adds value. If nowdroneswill begin to transport goods, then urban logisticswill freeze (from English to merge — "merge"— A.K.)- and there are numbers, and there are numbers. It will definitely be much easier to work with this than with live truckers.

The technology that I'm currently inspired by, and I hope something architectural will come from it, is new project Amazon, when there is a smart speaker in the center of the house that listens to all your questions and answers them. Kind of like Siri, only in the house. This technology will probably change the sense of space in the city more than any algorithm.

So the city will increasingly rely on software?

Exactly. Now I/O and various interfaces for obtaining information by humans are changing a lot institutionally. From my point of view, the cheap taxi service changes much more in my life than 90 percent of urban planning decisions. Taxis change a lot in my perception of the city. Despite all previous experience, with the advent of Yandex. Taxi and the competition of taxi services have turned out that our taxi drivers are polite, and the money is concrete, and they react quickly - completely different from some in New York.

The cheap taxi service changes my life much more than 90 percent of urban planning decisions

It seems to me that the most important service that could make huge profits from Uberification is prostitution. The hypothetical user is embarrassed, and perhaps that is why many people do not use the services of prostitutes - it seems to them something dangerous, scary and incomprehensible. Sitting on their phone, it would certainly be much easier for them. Of course, this would immediately take the bread away from the pimps and completely change the business. Simply colossal! I think this will happen in some liberal country soon.

Do you think people will be able to work with Big Data technologies personally in the future?

I think everything is heading towards this. Technological complexity will increase, and this is understandable, but practically, we will learn how to somehow package it correctly. Slick interfaces(from English sleek— thin, elegant— A.K.)today, to some extent, they simplify our perception of how everything happens. Here's a button, here's a little pipe - that's all. Today, the more you can hide from the average person without losing function, the better, because people are a little scared of all this complexity. Although the well-known technology, as in “Minority Report,” did not appear, tactilely the film very correctly describes what will happen now.

What will it be? What do you think big data will face in the near future?

They appeared as a kind of fashionable topic and are now slowly fading away, because the most obvious things have already been done. Next, it will be necessary to work out the technical mechanisms in the methodology - not in a romantic, but in a utilitarian form. In five years, I am sure, a fairly well-paid and, perhaps, rather boring position as some kind of digital analyst will appear in the mayor's office, at ministries and businesses.

At the same time, Big Data has a certain disease. There are people who understand what they are doing, and there are people who feed from it who do not really understand how Big Data works. The gap between professional technologists and people who understand why all this can happen always exists in any business, in any science, and this is certainly a problem. People who know the technological part and experiment with new solutions rarely do really useful things, and people who know how to apply these developments also cannot create a quality product alone. Therefore, the only way to development when working with Big Data is to find new ways of interaction between specialists.