Estimate the Pandemic-Era Percentage Drop in Taxi and Ride-Hailing Trips

Quick Overview

An estimation question asking by what percentage taxi and ride-hailing trips fell after COVID-19 arrived compared with before. It tests scoping the place, ride types and comparison window, breaking trips down by purpose, labeling assumptions, doing clean arithmetic and sanity-checking the final number.

Estimate the Pandemic-Era Percentage Drop in Taxi and Ride-Hailing Trips

Company: LinkedIn

Role: Product Manager

Category: Product / Decision Making

Difficulty: medium

Interview Round: Other

Estimate the percentage decrease in cab rides after COVID-19 arrived, compared with the period before it. The interviewer does not specify a place, a type of ride or a time window, so scope those yourself, then walk through your structure, each assumption and the arithmetic, and finish with one number and a sanity check. ```hint Not every ride fell equally Asking how "rides" changed in total is hard to reason about. Look for a way to split rides into groups that the pandemic affected for different reasons, and estimate each group on its own. ``` ### Clarifying Questions - Which geography: one city, one country, or the whole world? A dense city with heavy office commuting and tourism behaves very differently from a car-dependent region. - Do "cab rides" mean only street-hail and dispatched taxis, or app-based ride-hailing trips as well? - Which windows are being compared: the strict lockdown months, the partial-reopening period, or the time after restrictions ended, each against the same calendar months a year earlier? - Is the measure the number of trips, or the money spent on them? ### What a Strong Answer Covers - A pinned scope (place, ride types, comparison windows) that removes seasonality, chosen before any numbers appear. - An equation that ties the drop to drivers that changed for different reasons, such as remote work, closed venues, collapsed air travel and reluctance to share an enclosed car. - Explicitly labeled assumptions with round numbers, and correct arithmetic. - A cross-check through a second method or reference point, and a view on how sensitive the result is to the window chosen. ### Follow-up Questions - How would your estimate differ between a dense, transit-heavy city and a car-dependent one? - If you worked at a ride-hailing company, which internal data would you pull to replace your assumptions, and which kind of trip would you expect to recover first? - Why might app-based ride-hailing and traditional taxis recover at different speeds?

Overview: An estimation question asking by what percentage taxi and ride-hailing trips fell after COVID-19 arrived compared with before. It tests scoping the place, ride types and comparison window, breaking trips down by purpose, labeling assumptions, doing clean arithmetic and sanity-checking the final number.

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LinkedIn
Jan 14, 2025
mediumProduct ManagerOtherProduct / Decision Making
0
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Estimate the percentage decrease in cab rides after COVID-19 arrived, compared with the period before it. The interviewer does not specify a place, a type of ride or a time window, so scope those yourself, then walk through your structure, each assumption and the arithmetic, and finish with one number and a sanity check.

Clarifying Questions Guidance

  • Which geography: one city, one country, or the whole world? A dense city with heavy office commuting and tourism behaves very differently from a car-dependent region.
  • Do "cab rides" mean only street-hail and dispatched taxis, or app-based ride-hailing trips as well?
  • Which windows are being compared: the strict lockdown months, the partial-reopening period, or the time after restrictions ended, each against the same calendar months a year earlier?
  • Is the measure the number of trips, or the money spent on them?

What a Strong Answer Covers Guidance

  • A pinned scope (place, ride types, comparison windows) that removes seasonality, chosen before any numbers appear.
  • An equation that ties the drop to drivers that changed for different reasons, such as remote work, closed venues, collapsed air travel and reluctance to share an enclosed car.
  • Explicitly labeled assumptions with round numbers, and correct arithmetic.
  • A cross-check through a second method or reference point, and a view on how sensitive the result is to the window chosen.

Follow-up Questions Guidance

  • How would your estimate differ between a dense, transit-heavy city and a car-dependent one?
  • If you worked at a ride-hailing company, which internal data would you pull to replace your assumptions, and which kind of trip would you expect to recover first?
  • Why might app-based ride-hailing and traditional taxis recover at different speeds?