Design ETA prediction for Uber rides

Q: Design ETA prediction for Uber rides

This question evaluates competency in end-to-end machine learning system design for real-time spatiotemporal ETA prediction, encompassing feature engineering from heterogeneous data sources, uncertainty quantification, model selection, and production serving and monitoring.

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Question

System Design: Real‑Time Pickup and Drop‑off ETA Prediction

Context: You’re designing an end‑to‑end system that predicts pickup and drop‑off ETAs at trip request time for a large ride‑hailing marketplace. At request time, the driver is not yet assigned; predictions must serve within tight latency and remain reliable during traffic spikes, incidents, and rare events.

Specify the following:

Data Sources and Features
- Road graph and segment metadata
- GPS traces and map‑matched trips
- Historical segment speeds and travel times (time‑of‑day/weekday seasonality)
- Live traffic incidents and real‑time speeds
- Weather and major events
- Supply‑demand density and marketplace state
- Driver behavioral features (e.g., acceptance, cruising patterns)
- Device/network latency and dispatch latency
- Road closures, construction, and routing constraints
Modeling Approaches
- Baseline: map‑matching + dynamic shortest‑path
- Gradient‑boosted trees
- Spatiotemporal deep model (sequence/graph)
- How you represent routes for each model
- How you capture heteroskedasticity (variance changing with context)
Uncertainty and Calibration
- Produce 50% and 90% prediction intervals
- Methods: quantile regression, conformal prediction
- Calibration strategy
Training and Serving Architecture
- Feature store (offline/online parity)
- Streaming updates for traffic and incidents
- Drift detection and monitoring
- Online refresh cadence and deployment (canary, blue/green)
- Latency and availability SLAs
Leakage and Censoring Controls
- Exclude post‑dispatch signals
- Handling reroutes and cancellations
- Cold‑start regions and rare events strategies
Offline Evaluation
- Metrics: MAE/MAPE, P50/P90 error, coverage of 90% intervals, CRPS
- Stratified slices by city, time‑of‑day, weather, and surge
- Proper temporal/geographical holdout to avoid leakage
Online Evaluation and Rollout
- A/B test with guardrails: cancellation rate, pickup wait, driver idle time, reliability of intervals
- Rollout criteria and monitoring plan

Conclude with trade‑offs and select an MVP model for initial deployment.

Design ETA prediction for Uber rides

System Design: Real‑Time Pickup and Drop‑off ETA Prediction

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Design ETA prediction for Uber rides

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System Design: Real‑Time Pickup and Drop‑off ETA Prediction

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