We have heard a lot in the press recently about personal data (and some abuses thereof), and the forthcoming General Data Protection Regulation legislation for data protection and privacy for all individuals within the European Union. Data, however, can still be your friend — and in the context of public transport, can help operator and passenger (or rather "customer") alike.
A rail network is a very complex system. Its operation is reliant on many different areas and relies on infrastructure and having people and assets in the right places at the right times. Many variable elements (mechanical failures, weather, the behavior of the public) can significantly impact how the transport system can perform — and indeed services can return to normal after disruption.
Keeping it operating smoothly can be an intricate task, even without additional and often unforeseen disruptions to normal operations. Train operating companies have the added challenge of a changing world that is increasingly reliant on, and expectant of immediate information. The rail network already generates masses of data — but sources are often disparate or not in a form to be intelligently analysed. However, real-time access to data and information across the train network is becoming a game-changer for the industry and is something which will enable operators to efficiently manage issues and provide new levels of excellence in customer service.
When we choose to take a train, whether its for business or pleasure, we are contracting our travel requirements to someone else. Instead of handling situations such as road closures or traffic incidents ourselves, we become reliant on others to look after our journey — which includes telling us what is going on when unexpected. The impact of delays may be a small inconvenience, but it may also mean a missed flight or lost business opportunity. At one level or another, the customer is relying on the TOC to deliver them where they want to be and will almost always want to know how they are going to be affected by any delays.
To enable train operating companies to provide the service level they would like, there is a multitude of data inputs and outputs available across the train network. This information comes from a multitude of sources including timetables, real-time information about train positioning, staff availability, customer requests, problems on a route and even social media inputs such as comments on Twitter. Put all of this information together and analyze it using a "big data" approach can bring significant insight. The challenge the train operating companies face is how to collate data types, both similar and disparate, in a timely fashion and interpret them to gauge their response as challenges arise. Once understood, how do they then communicate relevant information to both staff and customers — in a personalized form ideally — so they can understand what is happening and what it will mean to them?
As with any big data analysis, the more data that is available, the better the possible system interpretation of the information and the easier it becomes to predict how scenarios will play out — potentially, even before they have happened. Historical data can empower train operating companies with the information they need to analyze past events and understand which operational strategies worked best to keep their customers happy. Systems can be preloaded with data relating to disruptions over a period of years, from both individual train operating companies and general rail network averages. Whereas the impact of weather is often unpredictable, issues such as a high-sided vehicle colliding with a rail bridge, for example, will have known repercussions based on the bridge location. Data analysis would be able to advise — on average — how long the situation may take to rectify and give train operating companies initial accurate information relating to the impact it will have on their operations. Combine that with real-time Milestone Plans from Network Rail, and the "expected" can become the "actual" as more information becomes available during the management of the incident.
A system that is to bring together and analyze huge volumes of data must communicate with existing rail systems, such as Tyrell IO and Darwin. It needs to handle the big data and varying scales of demand for information from the users. For example, during the snow disruption the level of impact is dynamic and, correspondingly, the level of queries to the system will increase as staff and customers need to assess changing circumstances. (In February/March 2018, during the peak of the disruption, we experienced a 25-time increase in demand via the JourneyCheck services we provide to train operating companies). A landslip across a line, on the other hand, is a more static event with more predictable consequences so, although the system needs to respond, the number of queries after the initial reporting, will most likely be fewer. Ultimately, the volumes of data generated are increasing at a tremendous rate and any system for the future needs to scale with demand.
Train operating companies are service providers and, as with any service provider, understanding customer needs has significant value. Indeed, we live in a world where the sale of products is being replaced by the provision of services — one only needs to look at the music industry to understand how customer demands and expectations are changing. As well as helping highlight and manage pinch points across services (operational benefits), transforming data into intelligent information offers rail companies a way of enhancing their customers’ experience and providing a new, improved level of service. This is, if you like, a form of monetization — certainly with regard to retaining customers long term, where different travel options may exist. And of course, through regulation, customer experience is considered when it comes to rail franchises. It is no longer just about operating a reliable service — it’s about innovation and customer service. (It’s a long time ago — perhaps 30 years — that "passengers" became "customers" in terms of the industry language used.)
Other forms of data can add further benefit. Bringing in social media, customer profiles and mobile location services opens the possibility of presenting information back to customers in an increasingly personalized way. With the necessary data input and analysis, the possibilities are literally endless. The system can be smart about who is informed about what. Event data, which may indicate increased expected service congestion in an area, or future weather forecasts could be used to help predict how the network will need to cope with the "unusual." Operators could also feed data in about decisions they are thinking of making so the system can feedback impact analysis prior to action being taken. The use of such a system helps avoid unforeseen circumstances. For example, a train operating company may be thinking of cancelling a service for strategic reasons that is not heavily utilized — minimizing impact to a wider range of customers. This decision may be being considered without the realization that the service had been cancelled for three weeks in a row and, although the seemingly obvious choice to overcome a problem that day, the repercussions may have a long-term impact on customer satisfaction and confidence. And whilst, very often, there is little choice about which trains do have to be cancelled, knowing that the same customers have likely been inconvenienced again, could allow for specific apologies being issued in the context of the repeated disruption.
Visibility across the network will also help ensure individuals requiring assistance with their journey do not become lost, even if they are not on their scheduled service.
Summary
Real and virtual worlds are merging. Technology is allowing us to manipulate data in real-time to provide valuable information on which strategic decisions can be made. Systems learn with increasing volumes of data and are even able to analyse and predict scenarios before they happen. Where are the limits of this new, data-driven world? Who knows! The one thing that is certain is that, as consumers use small, mobile devices to bring masses of data to their fingertips, their expectations of customer service are changing. We have a generation that demands to know what is happening in real-time and the technology for rail providers to deliver on this expectation is there today.
With all this in mind, the new Arrakis system from Nexus Alpha delivers a next-generation, cross-platform data management and analysis solution specifically designed for rail operators. It transforms data into useful information and connects services, staff and customers on a whole new level. It provides a platform for a rail network of the future and presents operators with the tools they need to deliver on or exceed customer expectations, as well as increasing the efficiency of overall operations.
Patrick McDougall is the CEO of Nexus Alpha Ltd.
020.7622.6816 • @NexusAlphaCEO • https://www.linkedin.com/in/patrick-mcdougall-468708/
