tldr: I used Uber Movement data to co-author a study that examined the impact of the Purple Metro Line on travel times in Bengaluru, along the much-awaited Baiyappanahalli- Whitefield extension.

When I was at Uber, part of my job involved finding ways to educate my stakeholders about some key metrics - such as this one: A trip of X km, took longer in Bangalore than Hyderabad, Delhi or Mumbai.

This meant the Bangalore cohort of drivers drove the same X km as their counterparts, but earned less, as they did fewer rides overall.

The reason? Heavy traffic along key office-corridors in Bangalore.

To dampen this effect, multiple regulatory levers could be pulled, impacting surge cut-offs, price floors, avg rs/km rates and so on. In our conversations with the government on these points, we realised the importance of showcasing traffic impact even with the expansion of public transit.

In 2018, Uber launched its free urban planning tool, Movement in Bangalore. To draw attention to the impact of the tool, I spear-headed the case study as the key internal stakeholder, working on the question: How does a metro line impact traffic in the surrounding areas:

The official title of the case study was: Examining the impact of Metro on travel times in Bangalore: Baiyappanahalli- Whitefield extension. The study used Movement data to calculate the impact of the expansion of the Purple Metro Line in Bangalore, comparing peak travel times from Bagmane tech park, Baiyappanahalli to Kadugodi, Whitefield between 1st January 2017 to 31st March 2017, against 1st January to 31st March 2018.

We discovered a ~13.5 % increase in the AM peak travel time, and a slightly higher increase of ~16.4% in the PM peak travel time between the two periods, suggesting an increase in congestion on the route, due to ongoing metro construction operations. We forecast a 21% decrease in travel time between the areas near both ends of the metro route, once completed, but cautioned that the volume of private vehicles was unlikley to reduce drastically.

Our concluding hypothesis was that this could be due to 2 factors:

1. Last mile connectivity was poor, hence private vehicle usage was higher, leading to more congestion.
2. Lack of available parking facilities along the metro line meant a user who used private transport for last-mile was more likely to drive themselves to their destination.

We began a dialogue with key stakeholders about a public-private last-mile program. The partnership did not materialise, but the case-study clarified the importance of parking and last-mile for public sector transport to flourish.