Leading in real timeAn investigation of the impact of real-time business on strategy and management.

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Transport providers arrive early to the challenges of automation

5. TRANSPORTATION

In 2011 New York’s Department of Transport deployed wireless sensors across Midtown

Manhattan to measure city-centre traffic speeds, and thereby congestion. Data were fed in real time to a control centre, where algorithms remotely adjusted traffic signalling, automatically smoothing jams and easing flow. The pilot was heralded as revolutionary by the then mayor, Michael Bloomberg. “We are now using the most sophisticated system of its kind,” he said, “to clear up Midtown jams at the touch of a button.” The system has since been rolled out citywide.

In truth, the use of real-time information and automated systems in urban transport has a long history. “We’ve had real-time systems for a long time,” says Shashi Verma, director of customer experience at

Transport for London (TfL), the local government body responsible for transport in the capital.

London’s computerised traffic signalling system SCOOT (Split Cycle Offset Optimisation Technique), which optimises traffic-light signals based on traffic flow, has been operating for decades. The first driverless trains came to the city in the late 1960s.

It is unsurprising, then, that companies in the transport sector are more advanced users of real-time data than most. In a cross-industry survey conducted by The Economist Intelligence Unit (EIU), 40% of executives from the sector say their organisations have successfully incorporated real-time information into up to half of their business practices. This is nearly twice the cross-industry average of 22%.

The transport sector has been an early adopter of real-time information and is wrestling with the challenges of incorporating automation sooner than most

Written by The Economist Intelligence Unit

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

% of transporation respondents

Justifying the investment required

Collecting relevant real-time information

Incorporating real-time information into existing businessprocesses

Designing new business processes around real-timeinformation

Having the skills to analyse and interpret real-time information

Incorporating the analysis of real-time information in strategicdecision-making

Choosing which decisions based on real-time informationshould be automated and which should be taken by employees

Responding rapidly to real-time information

30%

30%

40%

28%

32%

44%

48%

24%

Which of the following are the biggest challenges your organisation faces inusing real-time information?

% of transporation respondents

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According to three-quarters of transport executives surveyed, real-time data already play a major role in both operations management (76%), where real-time information can help optimise the delivery routes, for example—and customer service (76%). Examples of using real-time data to improve the customer experience include providing up-to-the-minute information about a vehicle’s location, to allow passengers to plan their journeys or to let delivery recipients know when a parcel can be expected to arrive.

There is still more room for improvement, and transport operators are looking to real-time data analytics to drive greater efficiency and resilience in their operations.

For example, monitoring the location of trains on a network in real time and adjusting their speed can allow operators to shorten the distance between vehicles—the headway—on their networks. “The average headway can be reduced from around three minutes to 80 seconds with no risk to safety,” says Andreas Mehlhorn, head of Siemens Mobility Consulting. “The line can handle 50% more traffic and cut its energy consumption by up to 30%.”

These achievements require automation: no human operator could react to real-time changes in the position of coaches fast enough to keep them at a safe distance.

Here again, transport companies are ahead of the pack: 78% of those surveyed by The EIU say they have automated business processes in order to respond instantly to real-time information, compared with 50% across all industries.

However, their advanced use of automation presents them with advanced challenges. Choosing which decisions based on real-time information should be automated and which should be taken by employees is identified as a challenge by 48% of transport executive surveyed, their most commonly cited challenge (see chart).

For TfL, one important factor influencing this decision is the complexity and significance of the decision in question. “If that decision is reasonably simple, then you can leave the computer to get on with it. If the decisions get complicated, then human intervention is always the right thing to do.”

This reflects in part the fact that the sheer volume and variety of the data available to transport operators is almost unique. Anything—from personal and vehicle location data, to ticketing data and scheduling, to weather and social media sentiment data—can be used somehow. Data are available from fixed and mobile sensors, but also from crowdsourcing. Google, for instance, provides live traffic information based on information gathered from Android phones. All of this could well lead to analysis paralysis.

TfL’s Mr Verma warns against collecting data for data’s sake: instead, transport organisations should start with the problem before looking to see whether real-time data could help. “It has to be for a purpose,” he says.

For example, TfL knows that every time it rains in London, demand on the tube and bus network goes up by about 4%. But what do you do with that information? “You can’t run more trains and busses every time it rains.”

That said, Mr Verma sees real-time data playing an even more crucial role in the future, by allowing TfL to predict service issues before they arise. “The real holy grail is predictive,” says Verma. “What you want to know from the real-time data is whether you’re going to confront a problem in five or ten minutes’ time. If you can act in advance of that problem occurring, then maybe the problem won’t occur at all.”

London’s Victoria Underground Station is one of the city’s most congested, and managing the flow of passengers at peak times is extremely demanding. If two trains arrive at the same time, causing 2,000 to converge onto the Underground line, the station will be overwhelmed, says Mr Verma. But closing the station is disruptive.

The ability to predict ten minutes in advance whether multiple trains will arrive simultaneously, and how full they will be, would allow operators to start taking advance action further ahead. “Being able to stop stations from closing would be a fantastic thing to do.”

“Using data to do things of that kind is an inexpensive way of squeezing more capacity out,” Mr Verma says. “That is the kind of research work that we’re engaged in right now. I have no doubt we’ll get there.”

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This article, written by The Economist Intelligence Unit and sponsored by Cisco, examines global organisations’ use of real-time information and its impact on strategy and management. It is based on a global survey of 268 executives, just under one-third of whom hold positions in the IT department, while 47% are members of the C-suite. Respondents were drawn from companies in the healthcare, transport, retail, healthcare, manufacturing and energy sectors, 49% of which have annual revenue over US$500m.

Information overload

The second most common challenge is incorporating the analysis of real-time information into strategic decision-making, as identified by 44% of survey respondents.