Fleet Vision
Real-time Fleet Tracking & Management System
Full-stack fleet management platform built with Next.js and Node.js. Real-time vehicle tracking via WebSocket, interactive map visualizations, driver performance analytics, geofencing, live video streaming, and multi-portal access for enterprise fleet operations.
Project Overview
Role
Full-stack development of production fleet platform. Built both frontend (Next.js/React) and backend (Node.js/Express) APIs with real-time communication infrastructure.
Scale
10,000+ concurrent users, 50+ REST API endpoints, real-time tracking of 5,000+ vehicles, 50+ database tables
Architecture
Microservices with separate databases: User Management Service + Data Service. Next.js frontend, Node.js/Express backends, dual PostgreSQL instances, Socket.io real-time, Docker deployment
Status
Production Live - Serving 100+ enterprise fleet operators continuously
Key Features
Real-time Vehicle Tracking
Live GPS location tracking with interactive map visualization. Vehicle markers update in real-time via Socket.io as coordinates change.
Performance Analytics Dashboard
Interactive charts and dashboards showing driver metrics, vehicle efficiency, and trip analytics with historical trend analysis.
Live Video Streaming
Real-time video integration from connected vehicles with playback and multi-stream support for fleet-wide monitoring.
Geofencing & Alerts
Configurable geographic zones with real-time breach notifications for unauthorized movements or violations.
Multi-Portal System
Separate dashboards for Admin (fleet management) and Drivers (personal performance) with role-based access control.
PDF Report Generation
On-demand performance reports with charts, metrics, and customizable templates for driver coaching and compliance.
Technology Stack
Frontend Stack
- Next.js 16 (Server Components, App Router)
- React 19 with TypeScript
- TailwindCSS + Radix UI (Components)
- Recharts (Performance Analytics)
- Leaflet Maps (Real-time Tracking)
Backend Stack
- Node.js + Express.js
- PostgreSQL Database
- JWT Authentication
- REST APIs (50+ endpoints)
Real-time Communication
- Socket.io (Live vehicle updates)
- Event-driven architecture
- Pub/Sub messaging
Deployment & DevOps
- Docker Containerization
- Cloud deployment (AWS/Azure)
- CI/CD Pipeline
- Load balancing
Architecture & Implementation
Microservices Architecture
Separated into two independent microservices communicating via APIs: USER MANAGEMENT SERVICE: • PostgreSQL database dedicated to user authentication, roles, permissions • JWT token generation and validation • Multi-tenant user isolation • Separate deployment and scaling from data service DATA SERVICE: • Separate PostgreSQL database for vehicle telemetry, GPS, metrics • Real-time data ingestion and aggregation • Independent scaling based on data volume • Time-series optimized schema for GPS readings COMMUNICATION: • Services communicate via REST APIs • Async events published to message queue • Decoupled deployments and updates Technical Challenge: Maintaining data consistency across two databases while ensuring real-time performance. Handling service failures without cascading issues.
Real-time Data Architecture
Socket.io maintains persistent connections from frontend to backend. When vehicle GPS location changes, data service publishes updates to all connected clients in real-time. Map components listen to Socket events and update vehicle markers without page refreshes. Technical Challenge: Managing thousands of concurrent WebSocket connections while maintaining data consistency across multiple dashboard views and microservices.
Interactive Map Implementation
Leaflet maps render thousands of vehicle markers efficiently using clustering and viewport-based rendering. Real-time position updates animate marker movement smoothly. Geofence boundaries drawn as polygon overlays with breach detection logic. Technical Challenge: Rendering 10,000+ markers with smooth animations and responsive geofence checks on every GPS update.
Analytics Data Pipeline
GPS telemetry data flows from vehicles → backend → PostgreSQL time-series tables. Aggregation jobs compute daily driver scores, efficiency metrics, and route analytics. Frontend queries use time-range filters to load aggregated data, preventing large dataset transfers. Technical Challenge: Computing driver performance scores across millions of GPS points monthly without blocking operations.
Multi-Portal Access Control
JWT tokens encode user role and assigned fleet scope. Admin users see all vehicles; drivers see only their assigned vehicle. Backend enforces data filtering at API level (WHERE driver_id = X for drivers). Technical Challenge: Implementing secure role-based access while allowing real-time updates without exposing unauthorized vehicle data.
Video Streaming Integration
HLS protocol streams video from on-vehicle cameras. Frontend player adapts bitrate based on network conditions. Multiple streams synchronized with GPS playback for incident investigation. Technical Challenge: Buffering and syncing multiple video streams without latency while maintaining map position accuracy.
Scale & Performance
Containerized services auto-scale based on WebSocket connection count. Database connection pooling handles thousands of concurrent queries. Frontend code splitting lazy-loads heavy modules (maps, charts) on demand. Technical Challenge: Supporting 10,000+ concurrent users with sub-second update latency across distributed servers.
Key Technical Implementations
Microservices Architecture with Separate Databases
Architected two independent microservices with isolated PostgreSQL databases for independent scaling and fault tolerance.
- User Management Service: Dedicated database for authentication, roles, permissions, JWT tokens. Scales independently based on login/auth traffic.
- Data Service: Separate database for vehicle telemetry, GPS readings, metrics, analytics. Optimized for time-series and high-frequency writes (1000s reads/sec).
- Inter-Service Communication: REST APIs + async events via message queue for loose coupling.
- Benefits: Independent deployments, failure isolation, technology-specific optimizations, separate scaling strategies, data-driven schema design.
Real-time WebSocket Infrastructure
Architected Socket.io event system to handle thousands of concurrent vehicle update streams. Implemented event namespacing to organize data flow (vehicles, alerts, chat) and reduce message payload size.
High-Performance Map Rendering
Implemented Leaflet map with marker clustering, viewport-based rendering, and smooth animations for thousands of moving vehicles. Optimized marker updates to prevent frame drops during real-time tracking.
Distributed Analytics Processing
Built background job system to aggregate GPS telemetry into performance metrics. Designed time-series queries to compute daily driver scores and vehicle efficiency without blocking real-time operations.
Role-Based Data Filtering
Implemented JWT-based access control with automatic data scoping. Ensured drivers see only their vehicle data while admins access full fleet visibility—enforced at both frontend and API levels.