I started with the obvious stuff, road graph, historical traffic, real-time data, but then kind of stalled when thinking about how all those signals actually get weighted and combined.
Start by clarifying that ETA is a prediction problem combining real-time and historical data, then walk through the data sources, modeling techniques, and trade-offs. Emphasize how you would measure and improve ETA accuracy as a product manager.
Pro tip: Acknowledge that ETA is a probabilistic estimate with confidence intervals, and discuss how Google balances accuracy with user trust—sometimes under-promising and over-delivering. Show you understand the business impact of ETA errors on user retention and partner relationships.
Define what ETA means for different modes (driving, transit, walking) and ask clarifying questions about constraints like real-time vs. historical data. Establish that ETA is a core metric affecting user trust and engagement.
List key inputs: historical traffic patterns, real-time GPS probes from Android devices, road network data, incidents (accidents, construction), weather, and time of day. Mention how Google leverages its massive scale for data.
Describe how machine learning models (e.g., gradient boosting, deep learning) predict travel time per road segment, then aggregate for the route. Include how models are trained on historical data and updated with real-time signals.
Cover trade-offs: accuracy vs. latency, personalization vs. privacy, and handling rare events. Discuss challenges like sparse data in rural areas, dynamic rerouting, and the cold-start problem for new roads.
Propose metrics: mean absolute error (MAE), percentage of trips within X minutes of ETA, and user satisfaction. Explain how to A/B test improvements and monitor for drift, ensuring continuous enhancement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.