Low-Level Design: Fleet Management System — Vehicle Tracking, Maintenance, and Route Optimization

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Core Entities

Vehicle: vehicle_id, vin (unique), make, model, year, type (SEDAN, VAN, TRUCK, ELECTRIC), status (AVAILABLE, ASSIGNED, IN_MAINTENANCE, OUT_OF_SERVICE), current_driver_id, current_lat, current_lng, odometer_km, fuel_level_pct, battery_soc_pct (for EVs), last_location_update. Driver: driver_id, user_id, license_number, license_expiry, status (AVAILABLE, ON_DUTY, OFF_DUTY), safety_score, total_trips, total_km. Trip: trip_id, vehicle_id, driver_id, start_lat, start_lng, end_lat, end_lng, start_odometer, end_odometer, distance_km, duration_minutes, status (PLANNED, IN_PROGRESS, COMPLETED, CANCELLED), started_at, completed_at. MaintenanceRecord: record_id, vehicle_id, type (OIL_CHANGE, TIRE_ROTATION, BRAKE_SERVICE, INSPECTION, REPAIR), description, cost, performed_by, performed_at, next_due_km, next_due_date. Alert: alert_id, vehicle_id, type (LOW_FUEL, SPEEDING, HARSH_BRAKING, GEOFENCE_VIOLATION, MAINTENANCE_DUE), severity (INFO, WARNING, CRITICAL), details, triggered_at, resolved_at.

Real-Time Vehicle Tracking

Telematics device (OBD-II dongle or integrated ECU) sends GPS coordinates, speed, fuel level, and diagnostics every 5-30 seconds via cellular (4G/LTE) or satellite. Ingestion pipeline: device → MQTT broker (IoT-optimized pub/sub) → location processor → Redis (current position) + Kafka (stream for analytics) + TimescaleDB (time-series history). Why MQTT: low overhead (2-byte header), designed for constrained devices, supports QoS levels (at-least-once delivery for critical data). Redis key: HSET vehicle:{id} lat {lat} lng {lng} speed {speed} fuel {fuel} ts {timestamp}. Geospatial index: GEOADD fleet:vehicles {lng} {lat} {vehicle_id} for proximity queries (“find available vehicles near depot X”). Trip detection: detect trip start (speed > 5 km/h for > 30 seconds) and trip end (stationary > 3 minutes). Automatic trip boundary detection without driver interaction. Offline buffering: if cellular signal is lost, the device buffers data locally (flash storage) and uploads in batch when connectivity returns. Timestamps are device-generated, so historical data is accurate even after reconnection.

Predictive Maintenance

class MaintenanceService:
    MAINTENANCE_RULES = [
        {'type': 'OIL_CHANGE', 'interval_km': 8000, 'interval_days': 180},
        {'type': 'TIRE_ROTATION', 'interval_km': 10000, 'interval_days': 365},
        {'type': 'BRAKE_SERVICE', 'interval_km': 30000, 'interval_days': 730},
        {'type': 'INSPECTION', 'interval_km': None, 'interval_days': 365},
    ]

    def check_maintenance_due(self, vehicle_id: int) -> list[Alert]:
        vehicle = self.repo.get_vehicle(vehicle_id)
        alerts = []
        for rule in self.MAINTENANCE_RULES:
            last = self.repo.get_last_maintenance(vehicle_id, rule['type'])
            km_since = vehicle.odometer_km - (last.performed_at_km if last else 0)
            days_since = (date.today() - (last.performed_at.date() if last else vehicle.commissioned_date)).days
            due_by_km = rule['interval_km'] and km_since >= rule['interval_km'] * 0.9
            due_by_date = rule['interval_days'] and days_since >= rule['interval_days'] * 0.9
            if due_by_km or due_by_date:
                alerts.append(Alert(
                    vehicle_id=vehicle_id,
                    type=AlertType.MAINTENANCE_DUE,
                    severity=AlertSeverity.WARNING if (due_by_km and km_since < rule['interval_km']) else AlertSeverity.CRITICAL,
                    details=f'{rule["type"]} due: {km_since:.0f} km since last service'
                ))
        return alerts

Proactive alerts at 90% of the interval allow scheduling during low-demand periods. ML-based predictive maintenance: train on sensor data (vibration, temperature, oil quality estimates from driving patterns) to predict component failure before interval-based triggers. Reduces unplanned downtime by catching failures early.

Driver Behavior and Safety Scoring

Telematics data enables driver behavior analysis. Monitored events: speeding (speed > speed_limit + 10%), harsh braking (deceleration > 4 m/s²), harsh acceleration (acceleration > 3 m/s²), sharp cornering (lateral acceleration > 3 m/s²), distracted driving (detected via phone movement patterns on mobile apps), seatbelt compliance (from OBD data on modern vehicles). Safety score (0-100): start at 100. Deduct points per event: harsh braking = -1, speeding = -2, harsh cornering = -0.5. Scores reset weekly (rolling window). Coaching: weekly reports sent to drivers and fleet managers. Persistent low scores trigger in-app coaching videos or in-person reviews. Gamification: leaderboard among fleet drivers, bonuses for top safety scores. Geofencing: define zones (customer sites, restricted areas). Alert when a vehicle enters or exits a geofence. Use polygon geofence (list of lat/lng vertices); point-in-polygon check on each location update (ray casting algorithm, O(n) per update where n = polygon vertices).

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