Estimation question

How much time is spent by people while waiting in their vehicles at traffic signals in the US?

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 estimation combining vehicle counts, traffic light behavior, and daily commuting patterns into one coherent national-scale figure.

State the funnel: number of registered vehicles in the US, average number of signalized intersections a vehicle passes daily, then average wait time per stop.
Assume roughly 280 million registered vehicles in the US, and that an average driver passes through about 5 signalized intersections per day (accounting for both drivers and non-drivers averaged out, maybe closer to 3).
Assume an average wait at a red light of about 30 seconds per stop, factoring in that not every intersection pass results in a stop, so apply a rough 50% chance of hitting a red.
Multiply: 280 million vehicles times 3 stops times 50% chance times 30 seconds gives roughly 3.5 billion vehicle-seconds per day, or about 1 million vehicle-hours daily.
Convert to a yearly or more intuitive figure if useful, and state the final answer as roughly 1 million hours of daily aggregate wait time as an order-of-magnitude estimate.

Builds the estimate through a clear chain of assumptions (vehicle count, intersections passed, stop probability, wait time) rather than guessing directly.
Explicitly accounts for the probability of hitting a red versus a green light, a detail many candidates skip, which significantly changes the result.
States every assumption clearly, e.g. 'assume a 50% chance of hitting red', so each link is inspectable and adjustable.
Converts the final number into an intuitive unit (aggregate vehicle-hours per day) rather than leaving it in a less meaningful raw form.

Assuming every intersection pass results in a full stop, ignoring the roughly 50/50 chance of hitting a green light instead.
Skipping the assumption-labeling step and presenting a single unexplained final number.

How would this estimate change in a city with heavy traffic versus a rural area?
What's the economic argument for investing in smarter, adaptive traffic signal timing based on this number?