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Fleet Trip & Utilization Tracker

Mock 4-hour take-home (Node/Express). No AI/ML, no external maps/geocoding APIs.

Data model — telemetry ping (input)

vehicle_id: string
timestamp: ISO-8601 datetime
lat: float
lon: float
speed_kmph: float
ignition: "ON" | "OFF"

Trip reconstruction rules

  • A trip starts at the first ignition=ON ping after a period of OFF (or after no prior data for that vehicle).
  • A trip ends when either:
    • ignition flips to OFF, or
    • the gap between two consecutive pings for that vehicle exceeds 30 minutes (treat as a dropped-connection trip-close, even if ignition is still ON).
  • Pings may arrive out of order and may be duplicated (same vehicle_id+timestamp) — dedupe and sort per vehicle before processing.
  • A ping with speed_kmph > 0 but ignition=OFF is bad data — decide how to handle it (log/skip/override) and be ready to defend the choice.

Required endpoints

  1. POST /telemetry — bulk-accepts an array of pings. Idempotent: re-posting the same ping twice must not duplicate a trip or distort distance.
  2. GET /vehicles/:id/trips — reconstructed trips: start_time, end_time, duration_min, distance_km (haversine between consecutive pings, summed), avg_speed, max_speed.
  3. GET /vehicles/:id/utilization?from=&to= — % of the window spent driving (in a trip) vs idle (ignition ON, speed 0) vs off.
  4. GET /fleet/summary — total trips, total distance, and the single most-active vehicle across the whole fleet.

Bonus (only after the 4 core endpoints are solid + tested)

  • POST /geofences (circle: center lat/lon + radius) + flag trips that enter/exit one.
  • Idle-alert: ignition ON + speed 0 for > 10 continuous minutes → surface as an alert list.

Constraints

  • No AI/ML, no external geocoding/maps API — pure math (haversine) from lat/lon.
  • Persistent store required — no in-memory Map/array as the source of truth. SQLite (zero external infra, fastest to stand up live) unless you want Postgres for closer-to-prod practice.
  • Node/Express.

Time box (4h)

  • 0:00–0:30 — design: data model, trip state machine, API contract
  • 0:30–2:00 — ingest endpoint + trip-detection state machine
  • 2:00–3:00 — metrics + utilization + fleet summary endpoints
  • 3:00–3:45 — edge cases (dupes, out-of-order, gaps, bad data) + tests
  • 3:45–4:00 — buffer

Grading lens

Correctness of the trip-boundary state machine on messy input; API design sense (idempotency, status codes, pagination if needed); ability to articulate tradeoffs (in-memory vs SQL, haversine vs Euclidean, ingestion scaling). </content> <parameter name="i">Write problem brief for fleet tracker exercise