LONDON, ENGLAND - NOVEMBER 18: A woman buys a ticket at Tottenham Court Road underground station on November 18, 2016 in London, England. London mayor Sadiq Khan has frozen pay-as-you-go fares until the end of 2020 and has urged the government to do the same for rail fares on services in and out of the capital. (Photo by Carl Court/Getty Images)
All aboard: data from smart travel cards are used to refine and improve transport systems © Getty

While people in more developed cities might be comfortable with data on how they travel being used to improve transport systems, passengers in emerging cities are also starting to benefit from the collection and application of digital information.

These data are collected in a variety of ways, from monitoring the use of pre-paid passes to improvising applications for smartphones that map commuter journeys.

The introduction of smart payment cards such as Octopus in Hong Kong and Oyster in London, for example, by public transport networks has done far more than end the fumble for loose change when buying a ticket. Smart cards record each time a commuter uses a network, telling transport operators when people travel, where they are going and what routes they take.

This has changed how transportation businesses plan their services and is more accurate than surveying passengers with paper travel diaries, says Lauren Sager Weinstein, head of analytics at Transport for London. When comparing Oyster data to these transport diaries, you would find that people had forgotten exactly where they had been, she adds.

The Oyster card, which can be used on buses, trams, river buses and suburban trains in the London area as well as the underground network, builds up a digital map of how passengers move around the system. But the different ways in which the card is used on trains and buses, for example, means this picture cannot be completed with Oyster data alone.

Ms Sager Weinstein says this is because on a bus or tram, you tap your pass on a card reader when you get on, but not when you get off, while on the underground network or trains you tap your card both when you start and finish the journey. However, she says GPS tracking and data science fill in the gaps.

“You know what time a card touches on a reader on a bus. You also know where that bus is, from the route and where it is travelling. You can see where that card taps next on the network. So then you see if that’s close to where the first bus runs, you can make an assumption they got off at the stop where their next tap is,” she says.

These data allow TfL to identify bottlenecks and to focus on providing sufficient services at peak times.

Outside developed cities, the expensive infrastructure required to collect these data does not exist, but data from other sources can be used for the same purpose. IBM Ireland’s AllAboard application, for instance, used telecommunication data from mobile phone operator Orange to plan transportation in Abidjan, Ivory Coast. Call data were used to map commuter journeys and make tweaks to the bus network that could cut commuting times by up to 10 per cent.

The data told IBM when calls were made and from where. While people could only be located to the vicinity of the nearest mobile antenna, this was sufficient to map commuter flows. Researchers could see patterns in the data they would not see in less digitised models, says Wendy Belluomini, director of IBM Research Ireland.

Previous data-modelling studies tended only to take into account where people lived and worked, but this new anonymised data reveals the messy complexity of “where people take their kids to school, where people go shopping, where their grandmother lives”, says Ms Belluomini.

The Ivory Coast government has not permanently implemented AllAboard, developed as part of a contest to show how data can be used to solve everyday problems for communities, but discussions to take the project further are continuing.

IBM has used a similar approach in Kenya. “The city of Nairobi had invested in new garbage trucks, but they weren’t getting the improvement to services they were expecting,” Ms Belluomini says.

Mobile phones were installed in the trucks and provided data on location and speed. Researchers found that improvised speed bumps were being used by locals to slow traffic, and this also damaged the trucks. This knowledge allowed Nairobi City County to address the problems.

A similar approach was used in the city of Cebu in the Philippines by the World Bank. Holly Krambeck, senior transport economist with the bank, says: “We distributed 300 smartphones to taxi drivers in Cebu [that reported] their GPS feeds to our open-source platform, OpenTraffic, with a map-based interface to allow the data to be queried.”

This allowed Cebu’s transport authority to complete travel-time surveys — which if done manually would take weeks — instantly and with greater accuracy. The project’s ultimate aim is to diminish congestion and improve road safety.

The World Bank has now expanded OpenTraffic by partnering with ride-hailing platform Grab, which provides the bank with GPS data that allow transport authorities to work out traffic speeds and volumes throughout the Philippines. There are plans to expand this pilot project across the whole of Southeast Asia, where Grab is a well-known brand. Other regions will be added to establish the world’s first, free global repository of open traffic data.

Such innovative use of existing information can provide big but inexpensive improvements to the lives of people in developing countries, Ms Belluomini says. “Expensive infrastructure won’t be making its way to the developing world anytime soon. So the question is what you can do with existing stuff, cheap stuff, to go after these problems.”

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