Joining the Dots

‘1740 from PBO to LGX cancelled’, ‘Points failure at Coleshill Parkway’, ‘Collision at bridge VIR176, ‘80% chance of snow in London at 1800’, ‘Lift out of order at CBG’, All London Underground services operating normally’.

What do all these pieces of information have in common? They could all impact John Smith’s journey to catch his plane from Stansted Airport. The rail network generates mass amounts of data, but a complete picture and assessment of services can only be gleaned if disparate pieces of data are joined together to give a meaningful picture.

Many pieces of data that could be useful to advise the best options for a journey or to manage the logistics of delay, for example, come from different sources, inside and outside of the rail network. The problem is that there is so much data, it is difficult to see the pertinent pieces to best answer a particular challenge.

Bringing this ‘Big Data’ together and analysing it offers rail companies a way of enhancing both their services to customers and the impact of any unexpected events or failures. Data such as timetables, real time information about train positioning, staff availability, customer request and even comments from customers on Twitter, can bring significant insight into operations. Indeed, collating, interpreting and clearly presenting results as pertinent information back to relevant onboard and control room personnel is a game changer for the rail industry.

Integrating historical data into the information management system with machine learning capability further offers a way to predict and test scenarios. The more data in the system, the more accurate the results!

So, back to our traveller, John. As he arrives at his home station, suitcase in hand and excited about his hard-earned vacation to warmer climes, he is dismayed to see his train is delayed. He turns to his phone to try to find out more information from the TOC app he has installed. Knowing where John is heading and armed with full information about situations across the rail network, the app suggests an alternate route that gets John to his plane in plenty of time. But is this just fantasy?

Of course, the story is completely fictional – but the capability from the TOC app is not. Through integration of data from similar and disparate sources, including communication with existing rail systems, such as Tyrell IO and Darwin, and with intelligent analytical capability, the cross-platform ARRAKIS smart communications suite is transforming the quality of communications between train operators and their customers. 

Limited only by the imagination of how different data sets can be used, the ARRAKIS platform combines automated rainbow boards (which show top level disruption on train routes), staff live departure boards, all Tyrell IO messaging – which includes station updates such as accessibility issues as well as route disruption, more severe disruption and changes to planned services (such as revised formations, carriage changes as well as cancellations, alterations and delays). It brings complete information together, not only to join the dots to interpret the impact of different pieces of data but to take customer service capability to a whole new level, helping make fairy-tale endings to journey stories even when problems arise.

The data is there. The technology is there. The possibilities are there. You just need to decide how you want to tell your story.

Discover ARRAKIS at www.nexusalpha.com