For many years, the capital has been predicted a transport collapse due to the rapid growth in the number of cars on its streets. However, the intelligent transport system introduced 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 (GUP MosgortransNIIproekt), who since 2013 has been in charge of developing transport analytics, building information systems and development integrated programs development of transport infrastructure, being in the position of Deputy Head of the Center for Traffic Management of the Moscow Government. At the BIG DATA 2017 forum held by the publishing house " open systems» On March 29, he talked 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 virtual and augmented reality tools can be used to solve our problems.
- 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 was approved by the Moscow Government on January 11, 2011.
Since then, the Department of Transport has been working on the development of transport infrastructure, using modern information systems.
Within the framework of the project, in 2014, a situational center of the TsODD 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 TV cameras, monitoring traffic conditions, visually informing road users, photo and video recording of violations of the management of urban ground passenger transport.
- Projects of which countries were taken as samples?
The experience of European states, 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 the transport infrastructure of Moscow is developing according to its own scenario, not to mention the load on the streets. Now, say, 683,000 cars are driving around Moscow.
- How is traffic situation management in the capital arranged now?
In recent years, within transport complex Moscow has created a number of IT systems that solve various problems in this area, including using Big Data.
static transport model, built in 2013, allows you to predict the situation for a long-term period, taking into account various options changes in road conditions. With its help, it is possible to calculate scenarios on a city-wide scale, whether it be long-term traffic closures or the commissioning of new overpasses.
This model, among other things, takes into account data on residents provided to us by various services: the number of people, their age, gender characteristics, social status, how many people are working, how many are not working, etc. Moscow is divided into so-called transport areas, and we we analyze where the residents of each such district go, why, at what time.
Thanks to the data obtained, we analyze the correspondence matrix - the totality of all "exchanges" of traffic between areas. 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 to help 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 in the traffic organization, what will be the result of blocking the road during construction or, conversely, the opening of a new one.
We monitor the current situation using a dynamic traffic model, which gives a complete picture of Moscow traffic in real time and allows us to respond to emerging problems. To do this, DTM aggregates data received from GLONASS sensors installed in public transport, photo and video recording 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 centers of accidents, places where traffic jams occur all the time; identify obstacles in the movement of passenger transport and eliminate them; to monitor the operation of mobile complexes for photo and video recording (the so-called parkons that fix offenses), to assess transport demand based on the daily correspondence matrix.
On the basis of the DTM, an interactive traffic map of Moscow was created, which displays real-time information on road congestion in points, on 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, on the basis of a dynamic model, the specialists of the TsODD 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 traffic jam by connecting to a camera that shows a real three-dimensional image of this area, which allows you to better understand the situation.
For citizens, this map presents 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 traffic situation?
The data received from the DTM is broadcast in real time by a number of radio stations, the Telegram messenger, and road signs. The Moscow 24 TV channel and its Internet portal m24.ru show a map of the current situation on the roads of the city.
Such informing is also a means of managing traffic flows. Muscovites see what the situation is like on the streets they are interested in, choose detour routes, consider the possibility of traveling by other modes of transport, for example, change from private 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 on the city streets, as well as increase their throughput, was launched in 2015. And already in the first year we managed to achieve considerable results.
I'll give you some numbers. There are now 4.6 million registered cars in the city, and the accident rate, according to the traffic police, is the lowest in the last ten years. In 2016, compared with 2010, the number of 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 passenger transport - by 7%. On the dedicated lanes introduced in 2016, passenger traffic increased by an average of 11%. The average arrival time of an ambulance has been reduced from 21 minutes to 8, almost three times, due to the fact that lanes for public transport have appeared, and buses and trolleybuses can give way to an ambulance, going into pockets at stops.
If we compare closer periods, then in 2016 compared to 2015, the number of accidents with material damage decreased by 18%, the number of accidents with victims decreased by 12%, and the number of cases of collision with pedestrians decreased by 14%.
- On the basis of whose decisions are the developments of the TsODD built?
We take the best Western developments. For example, the current traffic light control system is made on the basis of the Spanish solution, the static transport model is built on the German platform. But the solution that combines all these developments is domestic. All these systems were integrated by our specialists.
Based on the accumulated experience, we create solutions for managing the traffic situation 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 way?
We are on our way to a new management model. Last year, on the basis of an automated traffic control system, a pilot project was launched to automatically control traffic lights. Now the system is operating on Altufievskoye and Varshavskoye highways, as well as on Andropov Avenue, where, based on the DTM data on highway congestion, traffic light operating modes are automatically changed. This is not the case in any other city in the world. For example, even in the London transport management system Transport for London, traffic light operating modes are accepted by operators.
Now we set ourselves the task of extending the operation of this system to other highways. The difficulty lies in the fact that all the roads are interconnected, and it is necessary, while “clearing” some, not to stop traffic on others tightly.
- What new projects are planned?
We continue further development traffic accident forecasting systems. To carry out the forecast, it constantly analyzes the weather conditions, the characteristics of problematic road sections (bottleneck configurations, the degree of their reduction bandwidth), traffic flow indicators (average score of traffic congestion in the city and on the road section, flow speed on the road section, etc.).
We must be prepared for the future of driverless vehicles. In their navigators, information will already be loaded, for example, about the speed limit in a particular section, and the car will independently choose a safe speed mode.
The long-term prospects include the development of a public transport system, which should become an attractive alternative to a private car. Among other things, a developed transport infrastructure is an important economic factor that contributes to the competition of cities in attracting tourists, entrepreneurs, etc.
They will help unload roads and augmented reality systems. If it is possible not to go to the conference, but to watch 360° video from the workplace or even take part in it, and not through special glasses, but on the smartphone screen, then many will prefer this option.
Moscow transport and traffic management in numbers
More than 100 servers are installed in the data processing center, located under the building of the situational center of the data center, on which a total of about 2 PB of data is stored. Some of the information is constantly updated - for example, data received from cameras is stored on servers for seven days. Due to the constant growth of the data flow, it is planned to significantly increase the server capacity.
On an ordinary working morning, about 700,000 cars leave for the main "transport arteries" of Moscow.
At rush hour, 71% of passenger traffic falls on public transport, which is why it is their interests that the Department of Transport puts at the forefront.
Video recording cameras recognize up to 22 types of offenses - among them driving on the side of the road or a dedicated lane, turning from the second row, driving to a busy intersection, not allowing a pedestrian to pass, passing trucks without a pass, etc. They transmit information about 100 thousand traffic police to the traffic police per day. 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” comes” at 3 am.
Data has become an important asset, it is of considerable value in itself. At right approach to determining the owner and carefully building access to them, they can bring profit to all participants in the transportation process. But they can also become a bone of contention, writes the magazine.
“Data has become an asset. Data today is the gold and oil of the 21st century. The one who quickly learns to work with them, process them, cluster them, make products from them that increase added value, will be ahead,” Mikhail Mishustin, head of the Federal Tax Service, convinced his listeners at the session “Digital Transformation and Quality of Life. View from the regions”, which was held as part of the Russian Investment Forum in Sochi. He is talking about the so-called big data - and who, if not the head of the Federal Tax Service, where data on the income and property of millions of Russians are collected, understands all their value? But in fact, the official only repeated a phrase that can now be heard on hundreds of forums around the world from the heads of thousands of companies, including global ones. And the first question that arises is: since big data has become a valuable asset, then there should be rules that describe how to handle it, who owns it, is it possible to buy this data and at what price?
Big data technology implies the presence of three elements: huge amounts of data, computing power for very fast processing of this data, and special mathematical models that allow comparison of predetermined parameters, access to which was previously prohibited. This allows you to identify new, very often non-obvious connections and patterns and, based on them, make management decisions and make a profit (or, alternatively, solve socially important tasks).
In order to benefit from big data, technologies had to mature. More recently, companies have at their disposal computing power and algorithms that are able to quickly process huge amounts of data in real time, data centers where this data can be stored, the so-called Internet of Things is developing, which allows you to receive data from equipment in real time And various devices, performance is improving and the price of sensors that are used to collect data is falling.
Aleksey Fedoseev, Head of the Customer Service Department at Siemens Mobility, defines the limit from which data can be considered large: “1 million measurements, the so-called data points. From now on, we can implement analytical models that are based on the Big Data approach.”
The pioneers were the aircraft manufacturers. The value of big data, on the basis of which equipment malfunctions and failures can be predicted, is especially high in this industry. For example, now a Boeing 737 with two engines transmits 240 thousand terabytes of data in six hours of flight (the amount of data on paper in the Lenin Library is larger, but not by much - about 84 times). We are talking about the removal of several hundred thousand parameters per flight, although previous generations of aircraft collected only a few hundred of them.
Last year, the CEO of Tinto, a mining company whose fleet collects data from unmanned trucks, quarry drills, locomotives and at the port, said that the Central Control Center in the city of Perth receives 2.4 terabytes of data every minute (approximately 3,500 ha). terabyte per day).
Andrey Borodin, chief project engineer at the Design and Technology Bureau of the Digital Technologies Center of the Informatization Department of Russian Railways, says that, from the point of view of professionals, data is hot (that is, it gets processed immediately, in real time), warm and cold (unused, but left for storage).
“And even raw data is not unreasonably considered by many companies as an asset that can bring value, even if companies cannot use it now, to make predictive models or real-time response systems,” says Oleg Pyatakov, head of investment analysis at the company “ 2050. digital". He is sure that generating data for the sake of data is counterproductive, at least in the short term: “We need the ability to link data to each other (device / user IDs, timestamps), at least the minimum significance of data for those target parameters that we are trying to optimize, the ability to develop a control action . Indeed, in traditional (old) management systems, the situation was the norm when more than 95% of the collected data, for various reasons, was not used to make a decision.”
Russian Railways became one of the first Russian companies to start the process of digital transformation. And holding, of course, also works with big data technology. Naturally, the first area for their application is obvious - the regular collection of data from rolling stock and infrastructure using the Internet of things.
Siemens Mobility, which is a strategic partner of Russian Railways in this area, makes a clear distinction between two concepts - data and information. The data generated by the rolling stock and infrastructure, according to Aleksey Fedoseev, belongs to the operating organization: “As soon as we delivered technical systems Deutsche Bahn or Russian Railways, the data belongs to them.
Then, within the framework of service contracts, within the framework of individual contracts for the processing of this data, they are converted into useful information. For example, Lastochka trains, which are operated at the MCC, generate diagnostic messages about the technical condition of individual subsystems of the electric train. This data is aggregated and transmitted over a secure channel to a server in the Russian Federation. And only then, says Alexey Fedoseev, in the Center for Analysis and Data Processing, created jointly by Russian Railways and Siemens in February 2017, these aggregated data are converted into useful information.
The center's employees use analytical models that, based on the obtained technical parameters, make it possible to implement the concept of predictive maintenance and predict failures of critical rolling stock units, the expert says. An example is the processing of data received from a traction drive system. But not only. For example, the passenger door system is also monitored. When driving in the city train mode, the operation of the passenger door can affect the time spent by the train at the station, failures and failures in their operation can affect the violation of traffic schedules. Employees of the repair department of the Directorate of High-Speed Communications of Russian Railways have access to this information through the computerized maintenance system Cormap. The system is open, on its basis decisions are made on the issuance of trains to the line.
Predictive analytics models for the operation of high-speed trains supplied by Siemens for German, Spanish, Russian, Turkish railways, as well as Eurostar, have been improved over the past three to four years. The more data is processed, the more precisely models are functioning. The result is an increase in the technical readiness of trains. For example, the work of the Siemens Remote Monitoring Center on Velaro trains in Spain began a little earlier than with Sapsan trains in Russia. The models make it possible to predict failures of traction engines five to seven days in advance, which has led to the almost complete elimination of the possibility of disruption of the traffic schedule due to a decrease in traction. As a result, RENFE demonstrated its readiness to compensate 100% of the ticket price to passengers in case of a train delay of more than 15 minutes on the Madrid-Barcelona line. The reaction of passengers was not long in coming: the share of rail transportation in the passenger turnover in this direction increased from 20 to 61%, while air transportation decreased from 80 to 39%.
If we take the Russian experience in implementing similar models of predictive diagnostics for Sapsan trains, then, according to Alexei Fedoseev, the positive effects are obvious: on the Moscow-St. Petersburg line, the Sapsan train fleet has already covered more than 7 million km without delay due to technical failures that exceed 5 minutes (this is one of the parameters that the company uses to evaluate the level of reliability).
An important part of working with big data has become the creation of a so-called trusted environment - it is designed for the safe use of data and the exclusion of unauthorized access to it. For example, the "Trusted Environment of the Locomotive Complex" is being built to access data that will be generated by locomotives, consumers of this data - employees of the Russian Railways holding, service companies, rolling stock manufacturers and component manufacturers.
Relationships are not always based on a partnership basis. In this case, confrontation between the parties involved in the provision and processing of data is possible. How this can happen is demonstrated by the story that is developing right now with the Danish company Maersk, a leader in ocean shipping. Back in 2014, the company decided it would digitize its ocean shipping business. Maersk then reported that a simple sea shipment of chilled fruit from East Africa to Europe goes through a chain of 30 people and organizations and requires about 200 acts of interaction (transfer of documents, communication) between them, and 20% of the cost of delivering a consignment of goods falls on processing, document transfer and process administration. Maersk was going to drastically reduce costs in this area, where major changes have not occurred for 60 years.
In 2016, she decided on a technology and a partner, began cooperation with IBM companies as a carrier of advanced knowledge in the blockchain. The blockchain smart contract system, called TradeLens, began testing in 2017. In January 2018, Maersk and IBM announced a joint venture. We worked with partners to understand how to speed up the transfer of information and reduce the number of errors. It was announced that by the end of 2018 a full commercial version of TradeLens will be launched. By the middle of 2018, the system contained data on 154 million events (ship arrival dates, reports on the dispatch and arrival of containers, customs permits, commercial invoices and bills of lading, that is, documents on the acceptance of cargo by the carrier from the shipper), their number increased by 1 million each day - in general, TradeLens was ready for full operation.
At the test stage, 92 participants joined the system: shipowners, ocean carriers, shippers, ports (for example, the very large port of Rotterdam, through which up to 2/3 of ocean cargo for Europe passes) and customs. But at the same time, as the testing ended, it became known that other ocean carriers categorically refused to connect to TradeLens. And without the information of these players, the full use of the system is excluded.
It seems that such resistance came as a surprise to Maersk. In mid-November, the Danish company accepted an offer from top six competitors (MSC, CMA CGM, Hapag-Lloydand and Ocean Network Express) to join a non-profit association that will develop new standards for information exchange in the industry. André Simcha, CIO of MSC, the No. 2 ocean carrier, told reporters that his company would be happy to join TradeLens if the company became more open. In general, MSC likes the idea of working through a non-profit association much more, because, despite the promise of equal access to information, all intellectual rights to TradeLens are divided between IBM and Maersk. Carriers did not like the prospect of giving their data to the system, despite the fact that their main competitor will earn on them. Oleg Pyatakov still believes that Maersk has gone the right way and in the end they will defeat the proprietary solutions of powerful companies, and open standards without the participation of strong players will give way. But Maersk will have to compete for ownership of such a valuable asset as data. In November, the creation of a system competing with TradeLens was announced.
In Bashkiria, for the first time, “big data” was used in the analysis of the tourist flow. The State Committee for Tourism of the Republic of Belarus ordered a study from the Ural Center for Monitoring and Analytics, which was carried out on the basis of the dynamics of movements of mobile phone subscribers.
According to studies, from January to November 2018, 1.656 million tourists visited the republic, 60% of which are men aged 30 to 45, as a rule, employees of commercial organizations with higher education, with an income of 40 thousand rubles a month. The average length of stay is 3.8 days.
The peak of the tourist flow falls on the summer. In June 2018, the number of people entering was 179 thousand people, in July - 215 thousand people. The minimum figure was observed in February - 118 thousand people.
Guests came from various regions of Russia. The largest share of visitors - Moscow, Moscow region, Tatarstan - 11% each. Residents of the Orenburg region, Chelyabinsk and Samara regions accounted for the share of the tourist flow in 9%, 7%, 6%. Further, the Sverdlovsk Region and KhMAO - 3.8% each, the Tyumen Region - 3%, the Perm Territory and Udmurtia - each slightly more than 2%.
Foreign tourists came from neighboring countries, as well as India, Spain, Italy, Yemen, Germany, Turkey, Egypt, Nigeria, Israel, USA, Czech Republic, Saudi Arabia, Bulgaria, Iran, China and Finland.
A sociological study was also conducted in the form of surveys of tourists. 37% of respondents chose a hotel or a hotel to stay. 17% stayed with friends or relatives, 11% preferred hostels. According to the purposes of travel, the tourist flow was distributed as follows: trips to relatives (30%), business tourism (28%), health tourism (18%), sightseeing (12%), active (8%), pilgrimage tourism (0.2%) .
40% of tourists came to Bashkiria not for the first time. 20% came on the recommendation of friends (colleagues, relatives). 24% profit on a business trip. The least used sources of information when choosing the direction of travel for respondents were Internet portals (3.4%), social media(1.2%), media advertising (0.5%).
In the current 2019, the tourist attractiveness of certain regions of the republic will also be analyzed, the state committee informed.
"Geoanalytics using the capabilities mobile operators is an advanced method of counting the tourist flow. Currently, only Moscow has such experience, and let me remind you that the latter occupies the first place in the national tourist rating in the Volga Federal District, Bashkortostan - the second, ”said Azamat Galin, Deputy Head of the State Committee for Tourism and Entrepreneurship of the Republic of Belarus.
According to the Turstat portal, at the end of 2018, Bashkiria entered the Top 15 in the rating of domestic and inbound tourism, taking 13th place with the number of tourists over 2.5 million people (+13% compared to 2017).
These initiatives of the Government of Bashkiria are very interesting and useful for studying the tourist flow and planning their activities in order to promote the region's tourism products through the comprehensive provision of services to tourists, including using IT technologies.
By the way, Nizhny Nogorod is mentioned in the news. We previously reported that this city has implemented the "Guest Card" project, according to which it will be possible to track the movement of tourists visiting the sights of the city, their interests, tourists will be able to receive various discounts, as well as use public transport for free.
All these initiatives are being implemented in the regions isolated and isolated, without federal participation.
WHAT ARE YOU TALKING ABOUT?
The bottom line is that the issue of applying electronic visas for foreign citizens arriving in the Russian Federation is currently being resolved. According to the Association "Safety of Tourism", the use of such visas using special digital technologies without integrating the system of migration and registration of tourists in hotels and the services mentioned above using the "guest card" does not make sense. This is not a government approach.
In our opinion, a systematic, state approach should include taking into account all these elements. A tourist must register at the border once, having received an electronic tag, and then move around the country, register in hotels (already without migration registration), visit museums without problems, receive various discounts, use public transport for free or with discounts. And at the same time, this approach will allow both to ensure national security by recording the movements of foreigners, and to free hoteliers from the headache of registration and migration accounting, and for tourism management bodies in the constituent entities of the Russian Federation to receive information about the most popular objects of the region (city) and, on its basis, form tourist offers, thereby obtaining the maximum benefit.
AND EVERYTHING IS FOR THIS!
Namely, the Government Decree Russian Federation August 6, 2015 No. 813, which approved the Regulations on the state system of migration and registration registration, the implementation of which can significantly affect hospitality and increase the inbound tourist flow in general. This is exactly what the Chairman of the Board of the Association "Safety of Tourism" spoke about on December 06, 2018 in the Federation Council Sergei Gruzd participants of the round table on the topic "Actual issues of using electronic visas for foreign citizens arriving in the Russian Federation and improving the legislation of the Russian Federation in this area"
Recall that the issues of improving migration and registration records, simplifying the visa regime, developing and implementing a single biometric identifier for travel will be the subject of discussion within the International Forum "Tourism Safety" - TSIF - 2019.This Forum is a key professional event where representatives of authorities, the professional community and business discuss topical issues of ensuring tourism safety on one platform. The format of the Forum provides for 4 breakout sessions.
01.10.2018, Mon, 10:03, Msk , Text: Maria Sysoikina
The Safe Transport Innovation Center, established a year ago as part of the Moscow Metro, brings together developers of solutions for working with digital technologies. As part of the first strategic session of the innovation center, a discussion was held of new technologies offered by Russian companies, as well as initiatives already implemented by the center.
Community around Safe Transport
The Safe Transport Innovation Center has begun creating a community of experts and developers to exchange ideas and experience on the use of modern technologies in solving various transport problems for Moscow. Within the framework of the community, both those companies that already work with Safe Transport and new members will unite. At the first strategic session of the center, representatives of ABBYY, Maxima Telecom, Yandex.Taxi, Avito, Software Product and others shared their vision of the necessary technological changes in transport in Moscow, discussed the role of technology in the formation of new innovative services and offered ideas for personalizing the interaction of the city with its residents.
Big data is changing communications
The idea of creating the center was born in August 2018. The main goal of this initiative is the transformation of interaction with passengers, bringing communications with citizens to a new, personalized level. The analysis of big data helps to achieve the set goals. The innovation center has the ability to work with the data of organizations subordinate to the Department of Transport, performing its own research, testing hypotheses, doing work on building segments for targeted communication companies.
“We collect a lot of heterogeneous depersonalized data about passengers and, based on the analysis, we can provide important information to citizens in a targeted way,” explains the head of the Innovation Center Yuri Emelyanov. - Scenarios can be very different. For example, there are often changes in routes, repairs, traffic closures due to some events or activities. By analyzing the data, we can provide personalized information about the changes to those passengers who often move along these routes.”
Innovation Center Projects
The Center also has more large-scale projects, for example, an analysis of the satisfaction of districts of the city of Moscow in use ground transport. The center's experts conducted numerous surveys on this topic, analyzed the results and formulated initiatives to change routes, schedules and stops based on the results. The Center submits these initiatives to various steering committees held within the framework of the transport complex and, if approved, their implementation is carried out by subordinate organizations. Feedback based on the implemented initiatives, it again enters the innovation center, where the results of work and the degree of satisfaction of citizens are evaluated. This program started in March 2018 and has proved to be quite successful so far. Now the Center is actively participating in a similar program for the Moscow Metro.
Of particular interest is the project of the Center for analytical support of events within the framework of the World Cup. The center's experts analyzed passenger traffic on the days of matches held at Moscow stadiums (Luzhniki, Spartak, fan zone on Vorobyovy Gory), implemented satisfaction surveys as soon as possible after the game and developed recommendations to optimize the load on the city's transport system and make it more efficient organization of transport services.
Assessment of load distribution at the Luzhniki stadium. A fragment of the analytical report on the match Russia - Saudi Arabia, held on June 14, on the opening day of the championship
Support for mobile applications for citizens has become a separate area of work for the center. Safe Transport cooperates with a number of developers, including Infocompas, which develops the Moscow Helper app. “We are trying to support initiatives to create various services based on mobile applications for citizens. For us, this is one of the channels of communication with the population of the city,” says Yuriy Yemelyanov. – For example, experts of the Center together with developers mobile application Moscow Assistant are working to improve the algorithm for recognizing the state registration plate. The Innovation Center has many ambitious goals for the upcoming 2019.
Introduction
According to the forecast of analysts, 67% of companies from the aerospace industry are implementing projects based on Big Data, and another 10% are planning such projects. As for airlines, here the implementation of projects for February 2019 was announced by 44% of companies, and plans for such projects were announced by 25%.
These are the results of a study conducted in December 2017 by FlightGlobal regarding the role of Big Data for aerospace enterprises and airlines. Analysts also sought input on sharing aircraft condition data with manufacturers and repair and maintenance (MRO) companies. The study involved 300 professionals from the aerospace and aviation industries. Most of them are confident that Big Data technologies can improve the operational reliability and efficiency of airlines.
Approximately half of the respondents said that their companies use data sets on the condition of aircraft, which helps them make more informed decisions. In the short term, the share of such companies will grow to 75%.
Sharing data with OEM/MRO is still problematic. However, 38% of airlines believe that such a model can provide them with significant business benefits.
According to Honeywell's May 2018 Connected Aircraft survey, 47% of surveyed airlines plan to spend up to $1 million over the next year to connect aircraft to the Net for every aircraft they operate. Most of these companies plan to meet the amounts from 0.1 to 0.5 million dollars. However, in a five-year perspective, 38% of air carriers have already announced investments in the amount of 1-10 million dollars for each aircraft.
Until February 2019, when airlines invested in technologies related to aviation (connected technologies), it was primarily about providing satellite communications and Wi-Fi. Now, companies are ready to capitalize on the data they can get by using equipment directly on board aircraft. For example, such data could provide them with savings of 1% of the fuel consumed, which is equivalent to $ 50,000 per aircraft per year, analysts at Honeywell calculated.
Use of Big Data by foreign airlines
Big Data technologies are used to perform a number of tasks in the field of civil aviation. In this chapter, we will dwell in more detail on the main areas of use in aviation in a number of foreign countries. First of all, these are repairs and maintenance, ensuring fuel economy, creating digital twins, optimizing operations (including predicting flight delays), creating personal offers for passengers, etc.
Big Data and maintaining the airworthiness of aircraft
One such priority area in the near term will be the maintenance (MS) and repair of ships. For example, 88% of analytics respondents expect that it is in this area that they will be able to get the maximum benefits from the use of technology. Maintenance and repair are far ahead of all other areas in importance. Analysis and predictive repairs in aviation demonstrate their effectiveness and prove in practice that connected technologies work.
After maintenance and repair, respondents expect benefits from the introduction of related piloting technologies, including optimization of fuel consumption and aircraft turnaround times, as well as passenger service.
Thus, in the study "Sky High Economics: Evaluating the Economic Benefits of Connected Airline Operations", analysts note that connected aircraft can transmit data to the cloud or to ground servers, where this data can be analyzed using Big Data Analytics tools. This allows air carriers, for example, to detect malfunctions before they become major problems. The information obtained can be used to make better decisions and reduce costly aircraft downtime (Aircraft on Ground).
In addition, with the advent of predictive modeling, it has become possible to replace parts identified by analysis as requiring replacement before they fail, namely during scheduled repairs and maintenance. All this contributes to cost reduction and increases flight safety.
Digital twins. What it is?
The use of so-called "digital twins" is also closely related to the topic of predictive (proactive) repairs. However, in contrast to, for example, the oil and gas industry, where digital records are already being used by a number of large companies in practice, in the aviation industry this topic is still being discussed more at the level of experts and analysts.
In 2019, aviation industry experts began to actively promote the use of "digital twins": the leadership of the Swedish company IFS, a software developer for corporate clients, including those from the aviation industry, said in April 2018 that one of the technological innovations that can help airlines operate ships efficiently while reducing maintenance and repair costs is "digital twins" . Digital twins are virtual replicas of physical assets capable of demonstrating engine operation to engineers on the ground while the aircraft is in the air. To make this possible, engineers install thousands of data points during engine design and production. They are then used to create a digital model that monitors and controls the engine in real time, providing the necessary information throughout its life. life cycle such as temperature, pressure and air flow.
GE helped develop a digital twin for the aircraft's landing gear. The sensors were placed on the parts of the chassis most prone to failure. In real time, data such as pressure and temperature were transmitted to specialists, helping to predict failures or remaining service life. This data was compared with the data of the digital twin, which was subjected to similar loads.
To implement the predictive repair and maintenance model, two solutions will be used - an on-board data exchange system for flight operations and maintenance (FOMAX) and an off-board Skywise aviation data analysis toolkit. FOMAX, a server from Rockwell Collins, collects aircraft maintenance and performance data, automatic mode sends them to engineers and technicians. SkyWise, which runs on a cloud platform and provides data analysis, was developed jointly by Airbus and Palantir Technologies.
The FOMAX system receives all data from the FDIMU (flight data interface management unit) system located on board. FOMAX has the functionality of a 4G router: after the landing of the vessel, all data is transmitted using 4G Gatelink antennas to the Skywise analytical platform and analyzed by Airbus specialists. For analysis, Airbus specialists independently developed special models that can predict the occurrence of system problems. After the analysis, its results are sent to EasyJet specialists, who already independently make decisions on the need for predictive maintenance or repair. Using the information received, the air carrier's specialists can create algorithms that will allow in the future to predict the occurrence of a particular problem on any of the aircraft.
Aircraft model A320 with FOMAX are able to collect more than 24,000 parameters, that is, provide 100% collection of information from aircraft systems and components. Aircraft without FOMAX collect 400 parameters, i.e. 2% of the available information.
According to Delta's management, the company's predictive maintenance program is helping the airline significantly reduce disruptions: Over the past 12 months, the use of proactive maintenance has helped avoid 1,200 flight delays or cancellations.
The program uses data from various systems such as Boeing's Aircraft Health Management, Airbus and GE systems. At the same time, the basis of the airline's aircraft fleet is made up of aircraft that were developed even before the moment when data extraction and analysis became a "must-have" function. After analyzing the received data, the program develops recommendations for the replacement of parts and mechanisms. According to Delta Air Lines, the software used is 95% accurate in recommending replacement parts.
When presenting the solution, the startup indicated that in the spring of 2018 a pilot should be implemented with a major international air carrier. However, any additional information this topic has not been published.
Application of Big Data for analysis and forecasting of demand for air transportation
Aviation technology developer Saber Corporation, a solutions provider for the global travel industry, announced in November 2017 that it had signed a multi-year agreement under which Hong Kong Airlines would receive a MIDT (Market Information Data Tapes) solution. This decision is a database that provides access to historical and forecast (up to 11 months deep) booking data. Possession of this product allows airlines to analyze the impact of measures in the field of tariff formation, marketing programs.
Hong Kong Airlines expects to use the product in its plans to launch in the North American market. The product allows you to generate reports and has analytical functionality, which allows the user to identify the best channels for implementing a marketing strategy. Hong Kong Airlines will have access to the data of Saber agents around the world for all departures and destinations where the airline has a presence.
Big Data to improve customer satisfaction and personalization
British Airways, a top 10 passenger traffic company, has been using Big Data Analytics since 2013 to improve the level of service for its customers: The carrier collects various data on passengers in a special storage, and then uploads them to a program called "Know Me" . The purpose of the program is to learn and better understand the needs of customers, as well as to use the data accumulated during various contacts with these customers to improve their level of service.
"Know Me" contains various passenger data: online booking behavior, shopping preferences, seat preferences. All this information is automatically generated and automatically used on the next booking made by the customer.
The program works with the help of analytical software from Opera Solutions. It also uses Google Image Search, which allows airline employees to recognize high-value and high-flying passengers the moment they enter the airport or business box, and therefore offer them a first-class service.
Another major player in the market, Virgin Australia, announced in late 2017 that it was working on optimizing the performance of its machine learning applications. For these purposes, the company attracted a startup DataRobot. An American startup has developed a predictive analytics platform for quick creation and implementation of predictive models. The platform is already helping Virgin Australia reduce the time it takes to build predictive models by 90% while improving forecasting accuracy by 15%.
For February 2019, the airline is working on optimizing its Velocity Frequent Flyer loyalty program by introducing predictive analytics into it, which should support the company's customers when they choose the best time to use the points received. DataRobot is tasked with building forecasts / models of who is most likely to go on a trip, what price and what type of travel the traveler prefers. In general, we are talking about improving the level of service for participants in the airline's loyalty program.
Using Smart Chatbots
The first solution is a chatbot model that is close to human communication. At the same time, a machine learning application called LUIS (Language Understanding Intelligence Service) was used to improve the dialog capabilities. In addition, cognitive services are integrated into the chatbot, in particular face recognition. This will allow airline customers to request a list of movies starring a particular actor for viewing on board. To do this, you only need to upload a photo of this actor to the application.
The second solution is an application based on artificial intelligence for analyzing video content using machine learning. The platform was able to detect certain objects, scenarios, or age-restricted content, often required by airline content requirements. For example, artificial intelligence is able to detect content related to scenes of plane crashes or adult content and filter it out.
In April 2018, FoxTripper demonstrated the "traveling map" program for the first time. The program provides passengers with information about the places over which the aircraft flies, and allows passengers to make reservations at destinations. The data collected in flight, combined with the airline's passenger data, allows predictions to be made as to which products and services are relevant to the passenger.
Another interesting example- Gogo Air. This in-flight infotainment company is using artificial intelligence and machine learning to help airlines improve their experience. Gogo Air uses the Adobe Analytics series of tools, including Virtual Analyst, a machine learning tool, to collect customer information for a number of major airlines.
By providing in-flight entertainment content and Wi-Fi access, Gogo Air collects information about passengers using these services. This information is then processed and analyzed. As a result, airlines receive data that helps them improve customer service and, often, offer their passengers more targeted products. Airlines are learning what products their customers might be interested in while flying, what devices they use in flight, how much time they are willing to spend online, or what kind of entertainment they prefer on the plane.
Airlines use the data they receive to personalize services based on the situational context, for example, by adapting the screens of the infotainment systems in the aircraft to the client, depending on the length of the flight, the devices used by the passenger, and the destination.
Do not stand aside and the technology of catering on board. So, in April 2018 in Hamburg, Black Swan Data, a company developing solutions for data analysis, entered into a cooperation agreement with gategroup. The purpose of the collaboration is to analyze passenger data and social media trends to predict which menu on the plane passengers will choose. Passengers will be able to order and expect to receive their favorite meals after boarding. The pilot project of the two companies showed good results: It was possible to reduce food waste by 50% and increase productivity by 15%.
In May 2018, aviation solutions company SITA launched a baggage tracking and management system. The BagJourney technology she developed enables more and more airlines to manage their baggage operations. In the first six months of 2018 alone, more than 20 carriers chose this solution. SITA BagJourney is one of the main solutions that helps the aviation industry comply with IATA Resolution 753, which mandates baggage tracking at every stage of travel.
The SITA BagJourney solution is used every year to handle hundreds of millions of bags. According to users, the solution reduces the number of errors by 30%. BagJourney is compatible with a variety of hardware including mobile devices for scanning or stationary devices.
According to BahamasAir, one of the users of the solution, after implementing it within 7 days, they managed to complete the process of completely moving to mobile devices to track all baggage in two of the busiest destinations in terms of baggage - Nassau and Miami. According to the results of six months, the number of complaints about problems with luggage on the busiest route has decreased by 60%. The airline plans to implement the solution in all directions and expects that by the end of the year it will fully comply with the requirements of Resolution 753.
Interviews with experts
Application of Big Data technologies in Russian civil aviation
Working with client reputation is of great importance for transport companies, including aviation. Social networks allow you to collect real-time feedback from passengers and quickly respond to them.
The advantages of the system under consideration are the possibility of continuous monitoring of satisfaction with the company and interaction with users in social networks; ensuring security and identifying terrorist organizations, extremism and other problems; continuous improvement of the offer for the customer through analysis in social networks and the ability to communicate directly with the operator; maintaining the reputation of the airline through prompt contact with the audience in social networks; analysis of user preferences and preparation of individual product offers, as well as successful targeted advertising. More about the project -
Domestic DBMS Tarantool in the big data analytics project
Aeroflot has implemented predictive analytics algorithms as part of a platform project for analyzing and processing passenger requests in social networks. As a DBMS, a domestic development was used - the Tarantool solution from the Mail.Ru Group company.
The complex consists of a large number of modules that cover both functional business requirements and modules for integration into the existing IT infrastructure of Aeroflot PJSC and various channels for receiving requests (social networks, e-mail, official website, personal account).
The first module is responsible for identifying the client based on a comprehensive analysis of the data, both the text itself and the author's profile data. The number of calls can reach up to several thousand per day.
The second module is designed to search for duplicate hits. Copying text for placement on different resources or sending by mail. There is a definition of semantically similar posts in order to identify clusters that are incidents. Processing several such posts at once leads to a significant reduction in the workload of responsible employees.
The third module "Information occasions" is one of the most important in the system. Its main feature is to predictively identify dangerous posts even before the increase in activity begins. Thus, the embedded algorithms indicate a potential "info-bomb" and make it possible to offset reputational losses.
The estimated number of data accesses was several thousand requests per second with a required response of a couple of milliseconds. To meet the high requirements of the customer, such as the prescribed time limit of three seconds for enriching the address with various properties, the use of high-tech software was required. Based on the results of tests on performance, data storage quality and functionality, it was decided to use a domestic development - the Tarantool DBMS.
Tarantool is used in the Platform as an operational database in which requests are stored in the form of special data structures necessary for analytics algorithms. Extremely high performance and the presence in the database of such properties as secondary indexes and support for a large number of connections without performance loss made it possible to successfully implement the above functional modules without going beyond the set time frame.
| The use of domestic developments in such a large company as Aeroflot is extremely important. Russian software is often in no way inferior, and, as in our case, even superior to foreign counterparts. That is why Tarantool was chosen. And, of course, the important factor of import substitution is being fulfilled, which is one of the key priorities for our company for the coming years. Kirill Bogdanov, CIO, PJSC Aeroflot |
As a result of the implementation, the customer, using the Platform, significantly increased the efficiency of the process of processing complaints and customer requests by responsible employees of Aeroflot PJSC, dramatically reduced the time to deliver the request and the time to process / resolve the issue due to mechanisms for enriching the request with context, tone, topics (tagging), profile author, etc. All this is aimed at achieving a positive economic and reputational effect at almost all stages of the provision of Aeroflot PJSC services. Based on successful experience, all project participants will continue to use Tarantool software in their projects and strengthen partnerships
Technoserv implemented an intelligent customer segmentation system for Aeroflot
Technoserv confirmed that Big Data technologies are generally in demand in the transport industry, and this is confirmed by the increase in the number of projects using these technologies. At the same time, the topics of the projects, according to her, are completely different. These include the tasks of increasing the personalization of communications with customers, proactive equipment repair, demand prediction and other tasks solved using machine learning algorithms and analysis of large volumes of structured, unstructured and semi-structured data for both the airline and its partners. The speed of settlements has increased from 14 days to 23 seconds.
| We made a deal to buy an air ticket through an open API blockchain to a bank, but I am sure that such a scheme will be used by many companies around the world very soon. The blockchain platform allows you to significantly optimize business processes. It automates any settlement scheme, even very complex ones - for example, warehouse deliveries. With such a mechanism, human participation is practically not required: there is no need to issue invoices, conduct reconciliations, write acts. Potentially, suppliers of in-flight meals, fuel, airport services can be connected to the platform - all those companies with whom S7 Airlines constantly works and not only.(Aviation fuel smart contracts, AFSC) based on blockchain. The project made it possible to automate the planning and accounting of fuel supplies and is designed to increase the speed of mutual settlements when refueling aircraft.
According to the statement of representatives of Gazprom Neft, this is the first experience of using distributed registry technologies for the Russian aviation market. With their help, the airline was able to instantly pay for fuel directly when refueling aircraft without prepayment, bank guarantees and financial risks for the participants in the transaction. This approach increases the efficiency of financial transactions and reduces labor costs, according to the oil and gas company. Predicting breakdowns of S7 Airlines aircraft using machine learning and big data analysisAt the beginning of March 2018, S7 Airlines developed a predictive maintenance system. According to the company itself, it has become the first Russian air carrier to complete the development of such a system.
On initial stage it is used for Airbus A319 aircraft. In the future, the system will be adapted for the entire aircraft fleet. The predictive maintenance system involves the analysis of an array of historical data on the maintenance of aircraft and the operation of individual components. The software for data analysis and building a mathematical model was developed by S7 Airlines specialists together with the Russian company Datadvance, which specializes in developing solutions for predictive analytics. In March 2018, an array of data for the period from 2012 to 2017 was already available for analysis. These are data recorded in aircraft telemetry systems, databases of the S7 Technics aircraft maintenance and repair holding, and meteorological data. The main tasks that the company expects to solve with the help of predictive maintenance are reducing the number of flights delayed for technical reasons, improving flight safety and maintenance efficiency of ships, predicting the likelihood of possible breakdowns for each aircraft in the company's fleet. Several hundred RFID tags in each of the aircraft are attached to literally everything that is not nailed down - from life jackets to seat belts. Tags are also attached to heat-resistant gloves, megaphones, oxygen cylinders, masks, fire extinguishers, etc. The goal of the project is to speed up the inventory of emergency equipment that takes place after each flight. One of the flight attendants launches a special application on the tablet and walks around the cabin, scanning RFID tags. Each detected label is responded with a short sound signal, and at the end the application generates a report on the presence of all emergency equipment. The report is immediately uploaded to the server: SIM cards are installed in the tablets, and the cloud part is implemented on the basis of Microsoft Azure. If there is no equipment, this is immediately visible in the report, respectively, in this case, the command is not given to send apron buses with passengers and they are checked. Without equipment, the aircraft cannot be allowed to the next flight (that is, if there is not enough life jacket on board, it means that one of the passengers will be denied transportation). Manual inventory, on the other hand, takes much more time and effort: there are 189 vests under the seats alone, and they all need to be checked. Thus, thanks to RFID technology, Pobeda managed to reduce the minimum turnaround time for an aircraft from 30 to 25 minutes. This is one of the key KPIs in passenger aviation: the point is that the less time passes from arrival at the airport to departure on the next flight, the higher the cost-effectiveness of the airline, since the aircraft only generates income when it flies, and does not stand on the ground. With the size of the Pobeda's fleet of a dozen and a half aircraft, reducing the inventory time of each aircraft by 5 minutes makes it possible to operate at least one additional flight without increasing the aircraft fleet. Creation of an innovation center in the field of civil aviation to strengthen expertise in the field of Big DataIn 2017, Innodata, a Russian software developer in the field of innovative technologies, and the Russian IT University Innopolis created the Center for Innovation in Civil Aviation (CIGA). The purpose of the association is to develop a technological and digital presence in modern civil aviation, to promote the disclosure of the essence and significance of modern technologies that affect demand and supply for aviation industry players, and to integrate digital world innovations into current civil aviation technologies. In 2018, RAMAX Group became a partner of the Center in order to strengthen the existing expertise in the field of technology and develop specialized solutions for the aviation industry.
The main areas of activity are the development of existing and the creation of new solutions for the aviation industry, respectively. Center leads like educational activities, and project, whether it is the implementation of projects in the scientific and technical, innovation or information and analytical plane. CIGA is also open to pilot projects to promote advanced technologies and solutions and is ready to support development. Virtual reality to combat aerophobia, technology-based airport navigation virtual reality, behavioral analysis of employee activities in the information field, prediction of the purchasing power of passengers and the formation of dynamic recommendations for changing ticket prices, flight schedule planning and seasonal schedule optimization analysis, predictive passenger flow management, personnel management at airports, development of a system for selecting personal package offers for airline and partner services , as well as techniques for scanning the surface of aircraft during post-flight maintenance, runway analysis, overbooking level management, analysis of passenger interests and the formation of proposals for them. ConclusionThe examples discussed above show that airlines are no longer just aircraft, the carriers that we have become accustomed to. An important basis for their development is technologies that make it possible, for example, to personalize services. Individual offers that make the trip of each passenger as comfortable as possible. Finding travel information, booking tickets, search terms– any actions in the network leave digital traces that can be analyzed to form the most targeted package of services. In addition, working with big data allows you to increase customer loyalty, for example, by quickly responding to passenger requests. Even more data is generated by production systems. Airplanes, railroad locomotives and trains are the source of a huge flow of technical data that comes from sensors installed in engines and life support systems. A detailed analysis of these data allows you to identify and predict the need for repair of a particular spare part. Thus, they allow to increase the level of safety, as well as save significant funds for carriers. The required time for repairs is reduced and the aircraft can be used for its intended purpose for a longer period. The proposed material touched upon some of the possibilities and practical results of using Big Data technologies in the aviation industry, but in reality, there are more and more such opportunities for development every day. Read also
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