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Uber Movement Data Tool Reveals 21 Per Cent Peak Hour Travel Time Reduction

This case study by Uber Movement used data to calculate the impact of Purple Metro Line expansion in Bengaluru by comparing AM and PM peak travel times

Following launches in Mumbai and Delhi, Uber on 9th October 2018 announced ‘Movement’ in Bengaluru. Movement is Uber’s free urban planning tool and is an essential part of India’s comprehensive data-sharing exercise aimed to improve urban mobility in the cities of Delhi, Hyderabad, Mumbai and Bengaluru.

Christian Freese, Uber spokesperson said, “Through sustained efforts by our mapping team, we have been able to gather large amounts of traffic and congestion data for Bengaluru. We are excited to launch this platform and its benefits for Bengalureans to encourage better infrastructure, city planning and data driven policy solutions that will utilize technology to make the city more efficient. Together with the government, civil society and other stakeholders, we aim to continue playing a key role in solving for urban mobility challenges in Bengaluru.”

The case study

Christened “Examining the impact of Metro on travel times in Bengaluru: Baiyappanahalli- Whitefield extension”, this case study by Uber Movement is a brilliant example of AI platform tools that can help better urban planning across the country. The tool used data to calculate the impact, Bengaluru’s Purple Metro Line expansion will leave on the traffic movement and travel time.

The traffic tool by Uber compared data accumulated between 1st January 2017 to 31st March 2017, against 1st January to 31st March 2018 on the  Bagmane tech park, Baiyappanahalli to Kadugodi, Whitefield route.


Hinting towards increased congestion on the route, the study revealed an expansion of around 13.5 per cent in the AM peak travel time, and an expansion of around 16.4 per cent in the PM peak travel time. It also revealed that morning travel time on weekdays was 57 per cent more than peak morning travel time on weekends.

Based on the data calculations by Uber’s Movement tool, the study has also pointed towards an increase of 57 minutes in average travel time in Bengaluru, starting first quarter of 2020, giving concerned authorities ample time to plan ahead.

Speaking at the launch, Prof Gowda said, “The Government of Karnataka has always been innovation-friendly and will definitely embrace tools that can help enhance mobility planning in Bengaluru. By providing insights into how people get around the city, Uber Movement has the potential to further support informed policy decisions and strengthen a data-led approach to urban planning. In this era of big data, we can extract information around traffic trends and help measure the impact of extended metro lines, first/last mile connectivity options, traffic interventions, etc. It’s good to see the private sector sharing its data publicly in the interest of solving mobility challenges cooperatively. I wish Uber Movement all the best.”

The beauty of AI and data powered tools such as Uber Movement lie in the fact that not only can these help in a better approach towards planning, but these can also give governments and authorities ample amount of time to prepare for urban problems that may arise in the future.

In simpler words this case study is just an example of how helpful such platforms and tools can be, if applied intelligently.

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