I went top-down first, which in hindsight was probably the wrong call.
Break the problem into supply and demand components: estimate the number of e-scooters available in San Francisco, then estimate how many rides each scooter completes per day, and multiply by days in a month. Use a top-down approach with reasonable assumptions about population, usage patterns, and operational constraints, and clearly state your assumptions.
Pro tip: Show that you understand the difference between theoretical demand and actual rides by factoring in operational constraints like scooter availability, battery life, and rebalancing. Also, mention that you would validate your estimate with real-world data if available, such as Uber's internal metrics or public reports.
Confirm that 'e-scooter rental rides' refers to individual trips taken on shared e-scooters within San Francisco in a month. Clarify whether it includes all operators or just Uber's, and whether it's for a typical month or a specific one.
Determine the number of e-scooters deployed in San Francisco. Use population (approx. 900k) and adoption rates, or regulatory caps (e.g., SFMTA permits allow up to 10,000 scooters). Assume a reasonable number like 5,000-10,000.
Consider usage patterns: each scooter might be rented 2-5 times per day, depending on demand, battery life, and rebalancing. Use a conservative average like 3 rides per scooter per day.
Multiply the number of scooters by rides per day by 30 days. For example, 7,500 scooters * 3 rides/day * 30 days = 675,000 rides per month.
Compare with known data: San Francisco has about 500k adults; if 5% use e-scooters monthly with 2 rides each, that's 50k rides, which seems low. Adjust assumptions to ensure the estimate is plausible and within an order of magnitude.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.