Big data and intelligent transport systems easing congestion and increasing adoption of public transport
50 years ago decisions like where to build a bus stop - a place that physically aggregates people wanting to get on bus, or which routes the buses should ply on, were largely intuitive ones. Planners had some sense of how traffic flowed, and based on rudimentary data such calls were taken. The possibility that a bus stop was located at a spot that did not have enough demand – and or a designated route – were highly likely. These decisions of course, had profound impacts on commuters and congestion levels.
Today, technology has the ability to acquire, aggregate and crunch people’s travel pattern data, which can in turn address two of India’s biggest transport challenges – congestion and low adoption of public transport.
Reducing congestion
Take congestion, first. Congestion is a result of both, too many cars on roads and mismanaged traffic patterns. But imagine what you would do if you could capture travel histories of millions of commuters, aggregate it and match it with adequate supply. Yes, you can help managed traffic using data patterns.
Accurate and real-time data of this kind is fundamental to this and can be leveraged to make traffic management more responsive by reducing wait times, overall journey times, and congestion. In China, Didi Chuxing, the world’s largest mobile transportation platform, has deployed Didi Smart Brain – a smart solution that facilitates real-time data leveraging cloud computing and AI-based technologies, to improve transportation infrastructure in cities, including traffic flow measurements and smart traffic signals.
Elsewhere in the world similar experiments are underway. For example, The University of Arizona is partnering with Brazil’s fifth largest city, Fortaleza’s bus service, to help address congestion woes by leveraging big data in a unique way. The researchers tracked the number of people that rode a bus, and when and where they boarded, using data collected from the cards that passengers scan to ride. They also analyzed data from the GPS trackers on each of the city's 2,200 buses, which log location information every 15 to 30 seconds.
Using this data, the teams were able to write out algorithms that helped derive exactly how much time it took for a bus to move from one bus stop to the next. Then they were able to determine how fast a bus moves and where delays happen. As an outcome of this data analysis, the teams were able to develop a dashboard which Fortaleza’s city planners now use to make decisions. They are now able to better determine where to add buses, dedicated bus lanes, or more stops and terminals, along the city's 320 routes to help cut down on delays, which presently can last anywhere between 15 minutes to several hours.
Driving up the public transport adoption rate
The second challenge we face is the abysmally low adoption of public transport and concurrent high volume use of personal cars. A 2017, KPMG estimate, which measured public transport share in total trips, across select countries, showed that while Brazil was at 29% and Singapore at 86%, India was a low 7%. While there is indeed a supply side issue, where we do not have enough public transport assets on per capita basis, yet data driven public transport management can help deploy existing assets in more efficient ways. This could significantly improve commuter experience having a direct impact on adoption of public transport.
The good news is that experiments are underway in India. In Bangalore, The Centre for Internet and Society, along with universities of Manchester and Sheffield, conducted a study that was recently implemented by the Bengaluru Metropolitan Transport Corporation (BMTC). The study, which took three years to reach initial operational status in 2016, now covers more than five million daily passenger journeys undertaken by BMTC’s 6,400 buses. The project focuses on three aspects that can significantly improve the service, thereby benefitting both BMTC and customers.
These are: vehicle tracking units that continuously transmit bus locations using the mobile cell network; online electronic ticketing machines that capture details of all ticketing transactions; and a passenger information system linked to a mobile app, to provide details such as bus locations, routes and arrival times. BMTC has also piloted a user-friendly mobile application available on Google Play, which allows tracking of buses in real-time, including giving their estimated time of arrival at a specific bus stop. This app provides bus timetables, route maps and a trip planner to encourage reluctant users to make the shift to smart public transport, rather than use their personal vehicle.
According to the estimates by IIM Kolkata logistics firm TCI, congestion costs India 21.3 billion a year. At the same time, the availability of public transport is very low in India compared to other developing and developed countries. India has just 1.2 buses for every 1000 citizens, including private buses, example. Big data gives us a way out this gridlock and the chance to use our limited transport assets efficiently as we rapidly build our urban mobility landscape.
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