Estimation question
Estimate how many trains operate in the NYC subway.
- Estimation
- Easy
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What this question tests
Tests structured estimation for a specific, real transit system using ridership and train-capacity assumptions instead of a guessed number.
How to approach it
- State the approach: estimate via peak-hour ridership divided by train capacity and frequency, since this is more grounded than guessing train count directly.
- Assume the NYC subway carries roughly 4 million rides on an average weekday, with peak-hour ridership concentrated in a few hours, say 20% of daily riders in the morning peak hour.
- Assume an average train car holds about 150 people at capacity, with roughly 8-10 cars per train, giving roughly 1,300 riders per train.
- Divide peak-hour riders (roughly 800,000) by capacity per train (1,300) to estimate roughly 600 trains actively running to handle peak demand at once.
- Sanity-check against the known public figure that NYC's subway fleet has roughly 6,400 cars total, implying a plausible few hundred trains active simultaneously during peak service, consistent with the derived estimate.
What a strong answer includes
- Uses a demand-based estimation method (ridership divided by train capacity) rather than guessing the fleet size directly.
- Explicitly reconciles the estimate against a known public figure (NYC subway's total car fleet), showing critical self-checking of the result.
- States every assumption clearly, e.g. 'assume 150 riders per car', so each link in the reasoning is inspectable.
- Distinguishes trains actively running at peak from the total owned fleet, which includes reserve and maintenance trains, a nuance many answers miss.
Common mistakes
- Guessing a train count without any derivation from ridership or capacity data.
- Confusing 'trains operating at peak' with 'total trains owned by the system', which are different numbers.
Likely follow-up questions
- How would this estimate change for off-peak hours versus rush hour?
- What's the largest source of error in your estimate, and how would you tighten it?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop