Estimation question

Estimate the number of bicycles required to start a bike sharing operation in a big city.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests structured estimation of fleet size using population, adoption rate, and utilization assumptions for a shared mobility service.

State the goal: estimate bicycles needed to launch bike-sharing in a big, dense city of about 1 million people.
Estimate daily potential riders: assume 5% of the population rides on an average day, about 50,000 riders.
Estimate rides per bike per day: assume each bike supports about 4 rides a day given rebalancing and downtime.
Compute base fleet: 50,000 rides divided by 4 rides per bike, about 12,500 bikes needed to meet daily demand.
Add a buffer for maintenance, rebalancing, and peak-hour clustering: assume 20%, bringing the total to roughly 15,000 bikes.
Sanity-check against known real bike-share fleet sizes in comparable cities, noting this ignores seasonal and weather variation.

Builds the estimate from a clear formula, riders divided by rides-per-bike, rather than guessing a fleet size directly.
States each assumption explicitly, adoption rate, rides per bike, buffer percentage, so the logic is auditable.
Adds an explicit operational buffer for maintenance and rebalancing, a real-world factor many candidates skip.
Sanity-checks against known comparable city bike-share fleet sizes to validate scale.

Guessing a total fleet number with no formula connecting it to ridership or bike utilization.
Ignoring rebalancing, maintenance, and peak clustering, which meaningfully increase the needed fleet.

How would this estimate change for a city with significant seasonal weather variation?
How would you determine the right number of docking stations versus bikes?