Big data versus big congestion: Using information to improve transport

McKinsey QHere is a brief excerpt from an article written by Carl-Stefan Neumann for the McKinsey Quarterly, published by McKinsey & Company. He explains how and why digitization in infrastructure networks could improve forecasting, promote reliability, and increase efficiency. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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Congestion—on the road, in the air, on the rails—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.

It doesn’t have to be this way. 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. 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 behavior in ways that improve the reliability of transport infrastructure and increase its efficiency and utilization. In fact, some of this is already happening.

o Israel has introduced a 13-mile fast lane on Highway 1 between Tel Aviv and Ben Gurion Airport. The lane uses a toll system that calculates fees based on traffic at the time of travel. To make it work, the system counts the cars on the road; it can also evaluate the space between cars to measure congestion. This is real-time pattern recognition of a very high order. The information is then put to use in a way that increases “throughput,” or the amount of traffic the road can bear. If traffic density is high, tolls are high; if there are few cars on the road, charges are cheap. This not only keeps toll revenues flowing but also reduces congestion by “steering” demand.

o In Brazil, aviation traffic has been growing fast for the past decade, and annual passenger traffic is expected to more than double by 2030, reaching more than 310 million passengers. Not surprisingly, airspace congestion is a growing concern. To deal with the problem, Brazil is introducing a system that harnesses GPS data to optimize the use of available airspace, enabling less separation between aircraft and shorter routes.

The usual practice has been to line up planes preparing to land in an airborne queue. Under the new system, each plane is assigned its own flight path. It may sound simple, but making the system work requires enormous amounts of data, as well as fast and sophisticated evaluation of the data. The distance, speed, and capabilities of each aircraft are processed in a way that results in the shortest flight path. Instead of queuing up on approach, planes can “curve in” much closer to the airport.

The first deployment, at Brasília International Airport, is saving 7.5 minutes and 77 gallons of fuel per landing, as planes fly 22 fewer nautical miles on average. Brazil plans to roll out the system to the country’s ten busiest airports. Initial impact estimates suggest that deployment of this system at North American airports could increase capacity 16 to 59 percent, depending on airport conditions.

o 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 fulfill 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. At airport-industry gatherings, there’s lots of enthusiasm about using big data from tracking passengers’ mobile devices for tailored information and management. Ideas include text-message alerts on when to go to the departure gate, taking into account individual walking speeds, and reduced security queues based on better short-term demand predictions or tailored shopping suggestions. At the moment, though, no more than a few dozen airports are actually implementing ideas like these.

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Here is a direct link to the complete article.

I also highly recommend Rosabeth Moss Kanter’s latest book, Move: Putting America’s Infrastructure Back in the Lead.

Carl-Stefan Neumann is a director in McKinsey’s Frankfurt office.

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