Analyze the Accident-Rate Spike

You are looking at a monthly line chart of the accident rate for Uber trips in a single city. The rate climbs steeply from June through November, then falls quickly after November. Your task is to investigate what could explain this pattern.

Assume the current KPI is defined as reported accidents per 100,000 completed trips in local time, but you should question whether that is the right exposure metric. Explain how you would analyze the trend.

In particular, discuss:

• how you would validate the metric definition and the data pipeline
• what additional data you would request
• plausible business, operational, seasonal, and measurement-related hypotheses
• how you would distinguish a true safety deterioration from denominator effects, reporting artifacts, or product-mix changes
• which statistical or causal methods you would use
• what actions you would recommend under different findings

Overview: This question assesses statistical analysis, KPI and data-pipeline validation, causal inference, and diagnostic reasoning for interpreting temporal trends in transportation safety metrics, testing the ability to separate true safety deterioration from denominator effects, reporting artifacts, seasonality, or product-mix changes.