Fifty years ago, decisions like where to build a bus stop—a place that physically aggregates people wanting to get on to a bus—or which routes the buses should ply on were largely intuitive. 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 place that did not have enough demand—and/or a designated route—was 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 transportation.
Congestion is a result of both too many cars on the roads and mismanaged traffic. 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 manage traffic using data patterns.
Accurate and real-time data 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 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 has partnered with Brazil’s fifth largest city Fortaleza’s bus service to help address congestion woes by leveraging Big Data. 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 analysed data from GPS trackers on each of the city’s 2,200 buses, which log location information every 15-30 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 the delays happen. As a result, the teams developed a dashboard that Fortaleza’s city planners now use—they are 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.
Drive up public transport adoption
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 can significantly improve commuter experience, having a direct impact on the adoption of public transport.
The good news is that experiments are under way in India. In Bengaluru, the Centre for Internet and Society, along with universities of Manchester and Sheffield, conducted a study that was recently implemented by the Bangalore Metropolitan Transport Corporation (BMTC). The study, which took three years to reach initial operational status in 2016, now covers more than 50 lakh daily passenger journeys undertaken by BMTC’s 6,400 buses. The project focuses on three aspects that can significantly improve the service: 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 app 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 estimates by IIM Calcutta and 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 1,000 citizens, including private buses. Big Data provides a way out this gridlock and the chance to use our limited transport assets efficiently as we rapidly build our urban mobility landscape.
Deepanshu Malviya, author is co-founder, Shuttl, the bus-aggregating platform