Remote Assistance Rate

Waymo: Compute remote assistance incidents per 1,000 trips for each city, and decide which city needs attention first.

Assignment

The Waymo operations analytics team is preparing a weekly review of how often rides need help from remote assistance, a team of people who give guidance to a vehicle when it cannot decide what to do on its own. They shared two exports — one with trips, one with remote assistance requests — for the week of 14 to 20 September 2026, the first week of service in newly opened areas of each city, and want the number of incidents per 1,000 trips in each city.

Deliverables

• Incidents per 1,000 trips for each city.
• For each city, how many of the requests were repeats within an incident.
• A short closing report of your findings for the operations team, including which city should be reviewed first and anything they should double-check before relying on the numbers.

Definitions.

• Repeated requests on the same trip within 2 minutes count as one incident.
• A trip belongs to the week in which it starts, from 14 to 20 September 2026.
• Incidents per 1,000 trips $= \dfrac{\text{incidents}}{\text{trips}} \times 1000$

Data Description

waymo_trips.csv has one row per trip:

• trip_id
• city
• trip_type
• started_at
• ended_at

waymo_remote_assistance_requests.csv has one row per request:

• request_id
• trip_id
• requested_at

Practicalities

You can solve this in Python, SQL, or a mix of both — StrataScratch notebooks support both languages in the same notebook.

Assume this data comes directly from the operations system and hasn't been cleaned, so review it carefully before relying on the numbers. When something looks off, work out what it is and decide how to handle it; when a definition or a final decision is genuinely ambiguous, choose a reasonable option and explain why. There's often more than one valid way to compute a result, so explain the approach you take and the reasoning behind your choices.

What's assessed: your results, and how clearly you explain what you see in the data and justify the choices you make.