## Problem You have a transit log of tap-in and tap-out events. Each event is `[customer_id, station, timestamp]`. A trip is completed when a customer taps in at station A then taps out at station B. Compute the average travel time for each unique `(start_station, end_station)` pair. ```python def average_travel_time( events: list[tuple[str, str, int]] # (customer_id, station, timestamp) ) -> dict[tuple[str,str], float]: ... ``` ``` Events: ("C1", "A", 100), ("C2", "B", 110), ("C1", "C", 150), ("C2", "D", 200), ("C1", "A", 300), ("C1", "C", 360) Trips: C1: A->C (50), A->C (60) => avg 55.0 C2: B->D (90) => avg 90.0 Output: {("A","C"): 55.0, ("B","D"): 90.0} ``` ## Follow-ups 1. Your implementation must handle events arriving out of chronological order. How does this affect your approach? 2. What happens if a customer taps in twice without tapping out? How do you handle malformed data? 3. The transit authority wants a live dashboard: as events stream in, update averages without recomputing from scratch. How do you maintain a running average? 4. A customer makes 10,000 trips on the same route. How do you keep your running average numerically stable (avoid floating point drift)?

InterviewDB Experience

Average Travel Time: Compute Mean Trip Duration Between Station Pairs from Tap-In/Tap-Out Logs

phone

Interview Experience

Problem You have a transit log of tap-in and tap-out events. Each event is [customer_id, station, timestamp]. A trip is completed when a customer taps in at station A then taps out at station B. Compute the average travel time for each unique (start_station, end_station) pair. Follow-ups Your implementation must handle events arriving out of chronological order. How does this affect your approach? What happens if a customer taps in twice without tapping out? How do you handle malformed data? The…

Full Details

🔒

Unlock all Instacart questions

Full insider details, leaked discussions, and candidate experiences.

or every company, $100/year →

About This Question

This is a candidate experience report from a instacart interview during the phone round.

It covers the following topics: Coding, Onsite, Phone .

Topics

Coding Onsite Phone

Average Travel Time: Compute Mean Trip Duration Between Station Pairs from Tap-In/Tap-Out Logs

Interview Experience

Full Details

About This Question

Topics

More Instacart Interview Questions