Fleet Management & Tracking System
Real-time fleet tracking with predictive maintenance, driver behavior scoring, and fuel optimization for 2,000+ vehicles.
Duration
7 months
Team Size
5 developers
Industry
Logistics
Category
IoT Solutions
Fleet Management & Tracking System
A comprehensive fleet management platform that provides real-time visibility, predictive maintenance, and operational optimization for commercial vehicle fleets.
The Challenge
A logistics company with a large vehicle fleet faced operational inefficiencies:
- Blind spots - No real-time visibility of vehicle locations
- Reactive maintenance - Breakdowns causing delivery delays
- Fuel waste - Inefficient routing and driver behavior
- Manual reporting - Hours spent on compliance paperwork
They needed a unified platform to optimize their entire fleet operation.
Our Approach
We built an IoT-enabled platform that turns fleet data into actionable insights.
System Architecture
- Vehicle Hardware - Custom OBD-II devices with cellular connectivity
- Real-time Tracking - Sub-minute location updates
- Edge Analytics - In-device processing for immediate alerts
- Cloud Platform - Centralized management and reporting
The Solution
Real-time Tracking
- Live map with all vehicle positions
- Geofencing with entry/exit alerts
- Route replay and history
- ETA predictions based on traffic
Predictive Maintenance
- Engine diagnostics via OBD-II data
- Maintenance scheduling based on usage
- Early warning for component failures
- Service history tracking
Driver Management
- Behavior scoring (speeding, braking, idling)
- Hours of service compliance
- Driver assignments and scheduling
- Performance gamification
Fuel Management
- Fuel consumption monitoring
- Unauthorized fuel usage detection
- Route optimization for fuel efficiency
- Tank level monitoring
Technology Stack
| Layer | Technologies |
|---|---|
| Hardware | Custom OBD-II, 4G LTE, GPS |
| Mobile | React Native, Offline-first |
| Backend | Node.js, PostgreSQL, Redis |
| Maps | Mapbox, OSRM |
| ML | TensorFlow, scikit-learn |
| Real-time | WebSocket, MQTT |
Results & Impact
The platform delivered significant ROI:
- 2,000+ vehicles monitored in real-time
- 25% reduction in fuel costs
- 35% fewer unplanned breakdowns
- 20% faster delivery times
Key Features
Route Optimization
The system considers multiple factors:
- Real-time traffic conditions
- Delivery time windows
- Vehicle capacity and type
- Driver hours remaining
Predictive Maintenance Algorithm
Our ML model analyzes:
- Engine diagnostic codes
- Historical failure patterns
- Vehicle usage intensity
- Maintenance history
Client Testimonial
"We went from reactive to proactive fleet management. The predictive maintenance alone has saved us hundreds of thousands in breakdown-related costs and delays."
— Fleet Operations Director, Logistics Company
Managing a vehicle fleet? Contact us to discuss IoT solutions.
Key Results
2,000+ vehicles tracked real-time
25% reduction in fuel costs
35% fewer vehicle breakdowns
20% improvement in delivery times
Technology Stack
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