About This Architecture

Real-time trip tracking microservice with event-driven scheduling and predictive delay detection. The system ingests trip events from API and message queue, validates active trips with trackers, and calculates updated ETAs using driver parameters like meal breaks and rest periods. Prediction logic compares current vs. registered arrival times against tolerance thresholds to classify trips as on-time, at-risk, or delayed, then persists status changes to active and historical tracking tables. This architecture demonstrates how to handle time-sensitive logistics data with configurable business rules and state transitions. Fork this diagram on Diagrams.so to customize trigger sources, tolerance parameters, or database schemas for your fleet or delivery platform.

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How do you build a real-time trip tracking microservice that predicts delays and manages ETA updates?

This diagram shows an event-driven microservice triggered by API and queue events, validating active trips and calculating updated ETAs using driver parameters like meal breaks and rest periods. The system compares predicted vs. registered arrival times against tolerance thresholds to classify trips as on-time, at-risk, or delayed, then updates status in active and historical tables.