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Big data Vs big congestion… digitization to the rescue?

Posted by SmartRail World Staff on Aug 3, 2015

Sunset Tracks (Arne Hückelheim)"...innovations can improve punctuality and reliability while accommodating up to 10 percent more traffic."

Congestion — on the rails or indeed on the road or in the air — wastes time, increases pollution, and is costly to society. Commuters in Brussels and London waste more than 50 hours a year in traffic jams; that’s the equivalent of more than a full week of work. Across Europe as a whole, infrastructure congestion costs 1 percent of GDP. In the United States, airport delays alone cost some $6 billion to the economy. Carl-Stefan Neumann of McKinsey & Company finds out more and argues that it doesn’t have to be this way and there is solution... 

In 2013, the McKinsey Global Institute concluded that, globally, $400 billion a year could be saved by “making more of existing infrastructure” through improved demand management and maintenance.1 That is where digitization, in the form of big data, can help. The collection and strategic use of information can improve forecasting and help to nudge behaviour in ways that improve the reliability of transport infrastructure and increase its efficiency and utilization. In fact, some of this is already happening and offers opportunities for big data and rail

Railway-infrastructure providers in Europe typically ask operating companies for detailed itineraries of the trains they want to run, and then the providers create a schedule that tries to fulfil every request. The system is well intentioned but rigid—and it doesn’t lead to optimal capacity usage or operational stability. In Germany, the great majority of cargo trains do not depart as scheduled, a fact that inevitably leads to complications down the track.

Recently, some railway companies have started to follow a more “industrialized” approach that uses big data. They are splitting track capacity across the network into “slots” of different speed profiles based on an analysis of past demand and are allocating trains to available slots as requests for capacity come in. Capturing these opportunities requires advanced planning techniques that can, for example, allow trains to swap slots along their itinerary in order to recoup time lost to operational delays. Such innovations can improve punctuality and reliability while accommodating up to 10 percent more traffic.

In spite of these (and other) encouraging examples of the integration of information and infrastructure, progress in general has been slow. Why are infrastructure providers so slow to integrate big data? And what can be done to speed things up? Economic viability cannot be the reason. The payback from investing in such technologies is usually much better than investing in equipment with similar ability to boost capacity.

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Based on conversations with industry practitioners, we have identified three significant barriers to leveraging information effectively to improve transport-infrastructure usage:

  • First, there is a lack of transparency. Transport infrastructure involves complex networks with many participants.
  • Another issue is how to divvy up the costs and benefits of sharing information; different players do not always have the same goals.
  • Finally, there are regulatory constraints. Infrastructure in many cases is a natural monopoly. Governments therefore have an important role to play—in ensuring that operations are fair and cost-effective, and in creating a regulatory environment that allows data to be collected and used while protecting confidentiality and privacy.

All three barriers are interdependent and therefore need to be addressed at the same time. Without transparency, there is no way to build trust and achieve equitable sharing. Without equitable sharing (and clear public benefits), regulators will not be sympathetic. Without responsible regulation, players will be reluctant to make their data available.

It’s no easy matter to get all the parties in an infrastructure network to work together. A leader is required. Governments have an obvious interest in making the most of existing infrastructure, so one option is a national or multinational government entity. But it could also be the main concession holder, such as an airport operator or railway company. Or it might be a combination, with the government setting goals and establishing the conditions on data use and sharing, and the concession holders setting up structures to put the data to work.

Using big data in infrastructure is a work in progress; in important ways, it is just getting started. To build momentum, one proven strategy is to launch a pilot program, perhaps at a single airport or railway station, that tests data strategies and documents the benefits. But perhaps the most important thing is simply to recognize the potential that information has to improve infrastructure. For more on this see the video below.

This is an abridged article, focussing on the widers aspects of Carl-Stefan Neumann's article. For the excellent full article, which we’d recommend you take a look at, click here.

Picture courtesy of Arne Hückelheim

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You may also be interested in...

Free e-book: Building the Digital Transport Network of the Future.

Expert focus: How Big Data is making tracks in the rail industry.

Big Data and the rail industry: An expert guide.

Why have 2,500 New York subway cars been dumped in the sea?

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