TACKLING BIG DATA IN A RAILWAY CONTEXT

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COMPANY INTRODUCTION

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A NEW WAY OF WORKING

Supeo has more than eight years of experience in developing and delivering IT solutions to the railway industry. Supeo specialises in project- and software development for railway operators, infrastructure owners, infrastructure maintainers and railway construction companies. At the core of this is Sitra, a suite of web and mobile applications that help railway operators and tenders deal with Document, Human Resource & Security, as well as Enterprise Asset Management. Sitra also manages ticketing and penalty fares from issuing to final payment, all aspects of safety compliance, supports ISO or other standardised certification processes and much more. Today, Sitra stands as the system of choice for 95% of the Danish private railway sector, positioning Supeo as Denmark’s lead provider. Sitra is a simple and intuitive system for maintaining all aspects of railway and infrastructure operations, but Sitra is more than just a system; it is a new way of working. Supeo Mission Statement: Supeo provides key IT services to the railway and transportation infrastructure sector with the aim to deliver the best up-to-date services in terms of quality and time to our customers. Supeo considers it of vital importance that customers view Supeo as an organisation with high credibility and integrity as well as good listening and problem-solving skills, delivering high quality services at a competitive price, so that our contribution to our customer’s organisations always exceeds their expectations. We call it a New Way of Working.

Authors

Troels Løve Hannecke CEO & Founder of Supeo

Benny Esmann JensenCEO Supeo Denmark

Editor

Miriam Swietek

TACKLING BIG DATA IN A RAILWAY CONTEXT

THE RAILWAY SECTOR PRODUCES A TORRENT OF DATA EACH DAY, AND THERE IS AN ENOURMOUS POTENTIAL WAITING TO BE HARNESSED THROUGH AUTOMATED DATA COLLECTION AND ANALYSIS.

Recent progress in computing, calculation capacity and artificial intelligence means that there are ever newer ways of accessing and harnessing that enormous potential. However, in order to gain any benefits from Big Data, a strategy by which to collect the data and a system with which to do it is necessary.

TYPES OF BIG DATAThere are three basic categories of Big Data:

• Collected, structured data – such as ERP data, track data, regularity data, etc.• Collected, unstructured data – such as manuals, error reports, procedures,

instructions, laws, etc.• Uncollected data that would become big data sets if collected – fuel

consumption, sensor data, etc.

There is a wealth of information being produced in transportation operations in general and railway operations specifically but its availability is uncertain unless specific action is taken to ensure it.

START WITH A STRATEGYThe first step, whether you are a small organisation or large, to getting and having control over your data is mapping what data are currently available – even if underused – in the organisation.

This includes looking at the ownership of the data, how it is accessed, sorted and filtered and by whom.

The next step is getting all systems in the organisation to comply to an overall policy which as a minimum should cover guidelines for purchasing new systems, including data ownership and cross-system integration, and a specific strategy for Big Data handling. Besides considering what data new system can deliver and the ownership of it and how can the data be used to your best advantage.

Your company IT policy should include:

Data ownership, collection and access

Cross-system integration

A specific strategy for Big Data handling

A guideline for purchases

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CASEA customer came to Supeo with a problem about a new type of report. New legislation dictated that the customer’s organisation had to create and deliver a report with a specific appearance and very specific content.

We very quickly identified that the report required information that were hidden in five different sources of data in the organisation. Furthermore, none of those five sources of data were able to communicate with each other or a central system in the organisation. Two types of obligatory data were not even being collected in the organisation at the time.

Selling a system to collect data from five different sources and generate reports based on those data was a very simple process. The real work ended up being assisting the organisation with mapping out their entire data generation process and locating the sources needed for the report and gaining access to the data.

We often see that contracts are signed wherein the buyer does not have full ownership, but where the supplier has ownership of something, either the interface or the data generated. Map how data are collected and accessed in your organisation and by whom.

If the systems are not geared towards handling data, or if they cannot work together with existing systems, a lot of data are not used, simply because they are not collected or not collected in a coherent, singular system that can create an overview of what exactly is going on. That is why Sitra, while created to maintain all aspects of railway operations - including Big Data – on its own, integrates with other systems.

One of the key aspects of an IT policy are guidelines for IT purchases. Today, ordering a new system within any organisation department at a low cost is very easy. It may not even be a large system, but a small system, questionnaire tool, custom system developed to solve one specific issue, and it could be implemented and used by the department without any consideration for how the system does or does not integrate with the rest of the organisation. Crucial parts of your organisation’s data can end up locked in an unstructured, poorly organised format, rendering it inaccessible.

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Buy technology that supports the processA structured approach to the systems and data in your organisation is therefore a necessary step on the way to harnessing the potential in Big Data. Once there is a policy in place, the basis for collecting data is finding a structured, machine-based format that adheres to the overall guidelines set down in your organisation’s IT strategy.

One of the most important factors when diving into Big Data extraction and utilisation is making sure that you have technology that supports the process. One of Supeo’s most ardent beliefs is that everything has to be shared, imported and exported in order to create maximum value. Cutting yourself off from a source of data or information - unless it is done in accordance with an overall strategy - because of subpar technology is cutting yourself off from value.

Using Your DataOnce there is a strategy in place and the process of acquiring new systems that adhere to the strategy or updating old systems to supply the data in a structured way has begun, the attention turns to using and combining data.

This section will deal with the decisions related to collecting data and will then move onto the topic of innovative ways of having data processed, in the section called Opening Data Sources.

Business Intelligence Systems/Business IT EnginesHaving created the strategy, there is a decision to be made about whether to hire a data scientist or IT provider to collect the data or whether to let in-house IT create a system. It is in this context, that one of the other buzz words become significant, namely that of Business Intelligence systems (BI).

There are a lot of solutions on the market, and the key thing to be careful of is to not get lulled into a false sense of security that an expansive (and expensive) solution holds all answers. Rather, it is important that a system makes sense to your organisation and the people who are designated users of it. It is not always a rule that the expansive BI package solution makes sense to the ordinary user, which means that there might still be a need to hire a data scientist or paying large consultancy fees to the system developer to ensure maximum usage and exploitation of the opportunities inherent in the system.

Opening Data SourcesAt the recent annual meeting in the World Economic Forum, the WE pronounced that Big Data is a part of the 4th industrial revolution. In terms of Big Data specifically, the WE argues that “Above all, we need data to be open”.While that option will be explored further down in this paper, there is another way of opening up access to your data that can still ensure data protection while creating new approaches to your organisation’s data set.

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University/Student cooperationThere are a number of universities that are known for having close ties to the business community, and where it is common to enter into partnerships on specific projects. These include both long-term cooperation between an organisation and a university or short-term projects between an organisation and a specific student.

In the context of Big Data, it may be more relevant to look towards the IT crowd rather than engineering students, when considering a partnership. The benefits of this strategy of student/university cooperation - besides being an active participant in the education of future workers - is that some projects can be completed at a fraction of the cost of a data scientist. Students, due to their unique and often fresh perspective can offer new insights or different ways of viewing things, which sometimes result in radical ideas and projects.

The potential drawbacks are: one, that the proposed projects simply do not fit your organisation’s need, or two, that it might be difficult to attract students interested in a railway/transportation organisation. If that becomes the case, then there is another option to consider, namely that of the open access promoted by the World Economic Forum.

Open DataAnother option that has been in use in other industries for some time is opening up access to your data to the public, which can also open up for a public-private collaboration when outside users combine railway data with e.g. commuting information provided by a municipal open access database.

It is important to note that it is possible to select or reserve data that will not be exposed to the public. Conversely, closing off too many data sets will limit the gains that can be achieved.

Open access databases are appearing quickly, releasing a range of information from education, healthcare, crime data, business info, transportation information, etc. Even the European Union has created an open data portal with access to some data about transportation.

A personal hope for the public transportation/railway sector is that it will be inspired by the actions of these data-creating mastodons. If you have a set of data, you can expose it. By exposing it to the public, you allow people to create apps as well data collection and usage programmes. Opening up data can spark creativity and sometimes results in innovative applications because the data get a life of their own.

The plus side of allowing open access to data is that it can both provide you with answers to needs and questions, and sometimes even innovative projects that highlight an alternate data set that suddenly becomes very useful. However, as with students/universities, there is also the small risk that some of the projects are simply projects that are not quite relevant to your organisation’s needs.

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Combine your collected data

Whether it is done by employees/IT specialists or students/open access, the natural progression to collecting and using data is combining data, and sometimes in a new way to provide new knowledge. One example is the proposed Green Line case below.

Case: Green Line The Green Line case combines seven sources of data to create knowledge and improvement to three different audiences while placing the train operator firmly in the minds of customers and stakeholders as a progressive forerunner in the field of environmental concern.

GPS data, track data, real time, fuel consumption, passenger counting, employee ID and train ID are combined to produce information to management, employees and passengers and even resulting in a new passenger app.

By altering the time scope for the reports, it is possible to get an overview of both mid- and long-term trends and short-term tendencies.

FIG 1. Output from the system must be simple and useful. In a few seconds, the management should have an overview as well as an indication whether to act or not taking into account regularity, punctuality and/or fuel consumption as well as other parameters. Similarly, if management wants specification on employee or the train type. Another interesting data extraction could be fuel consumption and maintenance costs, for example expenditures on brakes.

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Case: Green Line - continued By altering the time scope for the reports, it is possible to get an overview of both mid- and long-term trends and short-term tendencies.

Employees have the chance to see their own performance results compared with budget per equipment time in both real time and over a longer period of time. For example, daily diesel usage compared to previous results, or punctuality in relation to the schedule, as a form of nudging for improvement.

If an employee wants to review his/her own performance, it is possible to create a personalised view through customizable parameters such as fuel consumption over x time compared to e.g. regularity/punctuality.

The reports can encompass longer periods of time or even give a detailed daily view. Thus, an employee can get a quick overview using the app with simple visual cues, or generate a more detailed, personalised and customised report.

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Case: Green Line - Passenger informationPassengers get dynamic information about the environmental benefits of choosing collective transportation both for the particular trip and any trips taken in any given time frame (trip, day, month, etc.), resulting in an app allowing passengers to track their own environmental results in a visual way comparing to their own stated objectives for reducing carbon emissions, etc. Thus increasing visibility of the fact that public transportation trump cars in terms of the environment, but also increasing preference for using public transport.

The results can be taken even further by creating both employee/internal information boards such as the above, and passenger/in-train information boards. The employee/internal information boards can detail the overall fuel consumption and kilometres driven in e.g. a 24-hour period (or any given desired period), and relation to the results for the previous 24-hour period.

And the in-train passenger information boards show the number of passengers and their joint contribution to the environment in terms of for example carbon emission compared to travelling in a car.

This relates directly to how data can be used for both reflective improvement and nudging for improvement as described in the next section.

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Innovating your business

Having readily accessible collected data and different ways of analysing, combining and using the data means that there is a prime opportunity to optimise your decision making process and creating a notable difference in the operation data. This can be achieved both through nudging for improvement and reflective improvement.

Nudging for ImprovementOrganic improvement, or nudging for improvement, are opportunities for improvement manifesting through reporting shown in a visually compelling way, as with the employee/in-house information board in the Green Line case. Providing information in real time, or at least relatively fresh information, makes it more relevant to the intended recipients thus increasing the chances of the recipients reacting to and acting upon the information.

Reflective ImprovementReflective improvement relies upon interpreting data and setting up KPIs in order to act upon them. In short, it means checking the facts related to any given process in your organisation, studying those facts and acting upon them. Using data from a longer period of time in order to create action/improvement plans may be taxing in a fast-paced environment such as train operations, but the question is whether your organisation can afford not to take advantage of the knowledge hidden in Big Data?

Taking Advantage of the Fourth Industrial RevolutionBut there is no reason to limit operation improvement and business optimisation to the Big Data hidden in your operations as they are now. Recently, the World Economic forum named Big Data and Artificial Intelligence (AI) ‘the fourth industrial revolution’, describing it as a veritable ‘tsunami’.

In the area of Machine Learning (ML), there has been quite a lot of development in terms of picture recognition and acting on individual sensor data, but it is as of yet difficult in a larger context to find examples in the railway sector of successful applications of ML.

An underused – or maybe even unused – option is to load the entire expanse of e.g. maintenance manuals and regulations with an error report, so that when a problem is detected, ML suggests what the technicians should do to rectify the error and point them in the direction of the appropriate manual. Extending on the feature you could program the AI to look for coherences between rises and falls in all collected data sets. For example, the increase in fuel consumption would induces a trend where an increase in the usage of brake pads or other maintenance tasks as a direct result of this.

Supeo has some ideas about how to incorporate Machine Learning, but the question remains whether the costs outweigh the benefits of using humans for the tasks instead, such as the example with the drone described below.

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Drones to Drive Improvement – AI vs Mankind

A lot of resources are spent on having technicians check rail infrastructure, such as walking inspections of tracks wherein two or more technicians walk a length of tracks. There is a way to potentially combine visual inspections with machine-based inspections for cracked joint bars, hairline surface cracks, cross ties, fasteners, etc.

One way could be equipping a drone with e.g. infra-red camera and high-res camera and having it fly over the tracks on a daily (or weekly) basis. Not only will the drone’s camera equipment be able to spot errors or problems but using state-of-the-art measuring equipment combined and cameras, the drone is able to spot tendencies before they become problems. Besides being a significant increase in safety, it would also eliminate the bipedal walking element, freeing up employee resources for other tasks. In addition, any errors reports would contain GPS position information and picture documentation. Similar the usage of precious timeslots on the tracks for maintenance vehicles could be reduced using drones.

To go even further, a drone check could be used to check for botanical growth along the tracks or even track temperature to prevent sun kinks. Furthermore, a camera could be mounted at the back of each train to film the tracks. By comparing the visual data input with baseline track data (picture/map recognition and comparison) and recent drone temperature measurements, the AI/ML system could sound an alarm in the event of sun kinks or pre-alarms in case of anomalies requiring further inspection. Alternatively, a drone with an advanced thermal camera could indicate temperature development by overflying the rails during periods of extreme heat and warn the operation centre to lower the speed if an increased risk of sun kinks nears.

The challenge with Machine Learning and Artificial Intelligence in a railway context. The industry has to be willing to invest enormous resources before any significant benefits can be reaped, and that expenditure has to be compared and contrasted to the increase in safety and relative reduction in workforces before making an informed decision to go into Machine Learning.

Big Data, Big Value?

Big Data can be compared to a vast sea full of unexplored opportunities. However, like a fisherman looking to catch a specific type of fish, the best results are not achieved by casting nets at random, catching a jumble of different fish; nor is it efficient to simply trawl using massive nets because the sheer amount of fish will weigh down the net and even rip it. Likewise, with Big Data, it is a question of using the right amount of data, in the right way and at the right time that will create Big Value for an organisation.

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