According to the Oxford English Dictionary, big data is defined as extremely large data sets used to reveal patterns, trends, and associations. However one wishes to define it, the significance of big data is having a major impact on how businesses analyse their performance. It’s true of the insurance industry, which uses data to quantify risk; retail, which establishes the products that are performing best; and it’s certainly true of the rail industry, which uses data to streamline operations and deliver better services.
It’s not just in the study of passengers using the train networks that returns valuable insights, though. Harvesting data from the trains and trams that carry the passengers themselves brings together crucially important safety and maintenance data, while the travel apps that so many of us rely on – and which help create seamless journeys across transport modes – simply wouldn’t exist without the big data that underpins them. In this feature, SmartRail World explores the transformational effect that big data has had on transport, picking out four areas that demonstrates – if it wasn’t clear already – just how important it will be for the future.
Operating one of the world’s most advanced and complex transport networks that comprises subterranean and over ground trains, buses, ferries and trams, Transport for London (TfL) has a policy of sharing its data to benefit not just itself but the wider economy. By providing data “in an open, transparent and free-to-access way”, TfL’s strategy that’s been shown to work, after the research and audit company, Deloitte, revealed last year that UK capital’s transport operator had generated annual economical benefits and savings to the tune of £130 million, improving journeys, saving people time, supporting innovation and creating jobs.
Reportedly, more than 11,000 developers have registered for TfL open data and has powered somewhere in the region of 600 travel apps. One such individual that has manipulated some of the swathes of data is actually a resident of the UK’s second city, Birmingham. Matthew Somerville’s live map [below] is viewable through geographic and schematic displays and that demonstrates exactly the sort of things that can be achieved.
He has even made a map that replicates a scene from James Bond’s Skyfall in which computer code is overlaid on a complex computer-generated map.
Competition breeds success
On the other side of the world, in Australia, the New South Wales government is challenging the latest and greatest tech minds to use their talent to use its open data to create the app that could also improve the region’s fortunes. Launched to provide users of NSW’s transport infrastructure with innovative ideas from the first to the last mile, the MaaS Innovation Challenge template was an attempt to open the mobility marketplace and convince drivers from their cars.
With a similar programme that calls on participants to design apps to include smartphones, web services and Internet of Things (IoT) gadgets to make its network more accessible to all – including tourists and those with mobility problems – Tokyo’s has launched the second edition of its own open data challenge. Acknowledged by the competition’s organisers as one of the hardest transport networks in the world to navigate, the initiative is supported by Tokyo Metro and East Japan Railway Company. The winner of the competition will claim a one million Yen (£6,900) top prize.
The power of (AI) conversation
Over to France to see what the national operator there, SNCF, is doing to keep their customers up-to-date: an AI-powered chat programme that enables passengers to get answers to journey-related questions. Working in much the same way as the live digital assistants that are becoming commonplace on e-commerce sites, the difference here is that there is no need for it to be staffed by humans.
First trialled on a small scale in 2016, SNCF’s chatbot has three languages in its armoury: French, English and Spanish, but the operator has said that it would like to introduce more in the future, despite the high costs that would come with potentially having to implement a completely different alphabet. Powered by a conversational variety of AI designed for use in customer service environments, one major hurdle for any technology of this type is that it fully understands all customers regardless of accent, cadence or tone. Quite a challenge. According to SNCF, the chatbot will be released in early 2019, when it will tie in with its manned call centre for more complicated, bespoke enquiries.
Also using big data to plot reactive information onto a map layout, the work undertaken here by Emu Analytics seeks to make the tracks safer for those crossing one of the 6,000 level crossings dotted along the UK network. Providing more than just location information, the London-based data experts have developed a system that reflects the number of trains that pass over the crossing each day, types of rolling stock, the speed of the line and reported near-misses [pictured below].
The data behind the work has are collected manually during the working week, so can’t take into account sites that experience more traffic over weekends, or seasonal variations, so if there are any limitations to the system it is from the data itself. However, taking into consideration the huge developments in both price and performance of trackside monitoring equipment, it’s conceivable – to us at least – that this limitation could soon be consigned to history.
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