Design Real-Time Location Tracking

Product Context

RideWave is a consumer mobile app that lets users share live trips with friends, estimate arrival times, and receive safety alerts when a tracked device deviates from its expected route. The ML system must turn noisy mobile GPS pings into accurate real-time location state and short-horizon movement predictions.

Scale

Task

Design an end-to-end ML system for real-time location tracking in the mobile app. Your design should address:

1. How you would define the prediction tasks and success metrics for live tracking, map matching, and short-term ETA / route deviation detection
2. The full architecture from mobile event ingestion to online inference, including any multi-stage pipeline such as candidate road-segment retrieval, ranking, and re-ranking / smoothing
3. What features and labels you would use, and how you would build batch + streaming pipelines without introducing training-serving skew
4. How models are trained, deployed, refreshed, and evaluated offline and online
5. How you would monitor the system and handle major failure modes at scale

Constraints

• Mobile GPS is noisy, sparse indoors, and can be delayed or arrive out of order
• Battery cost matters: the app cannot increase GPS sampling aggressively for all users
• The system must support regional data residency requirements and minimize retention of precise raw coordinates
• Freshness matters: route deviation alerts should trigger within seconds, but false positives are costly
• Cost target: online inference and feature serving should remain efficient enough to support sustained 220K events/sec at peak