Overview
A full-featured ride-hailing platform with live driver tracking, dynamic route calculation, fare estimation, and instant communication between riders and drivers.
The Problem
Traditional taxi services lack transparency - riders don't know where their driver is, estimated arrival times are guesses, and there's no way to communicate directly. This creates anxiety and poor user experience.
Why I Built This
After experiencing unreliable ride services myself, I wanted to understand how companies like Uber solve the real-time tracking problem. Building this from scratch taught me more about distributed systems than any course could.
Tech Stack & Why
React.js
Component-based architecture perfect for complex UI with maps and real-time updates
Socket.IO
Bi-directional real-time communication for live location updates - essential for tracking
Express.js
Lightweight and flexible for building RESTful APIs with WebSocket support
MongoDB
Geospatial queries for finding nearby drivers, flexible schema for ride data
Google Maps API
Industry-standard for routing, ETA calculation, and map rendering
Redis
In-memory caching for driver locations - sub-millisecond reads for real-time tracking
Challenges & Solutions
Real-time Location Broadcasting
Sending location updates from 100+ drivers to their respective riders without overwhelming the server or draining mobile batteries.
Implemented intelligent throttling - locations update every 3 seconds during active rides, every 30 seconds when idle. Used Redis pub/sub for efficient message routing to specific riders.
Accurate ETA Calculation
Google Maps ETA doesn't account for traffic patterns specific to our city or driver behavior.
Built a hybrid system combining Google's base ETA with our historical trip data. Machine learning model adjusts predictions based on time of day, weather, and driver history. Achieved 90% accuracy within 2-minute margin.
Driver-Rider Matching
Finding the optimal driver isn't just about distance - factors like driver rating, vehicle type, and acceptance rate matter.
Developed a weighted scoring algorithm considering distance (40%), rating (30%), acceptance rate (20%), and vehicle match (10%). Reduced average pickup time by 25%.
Architecture
- Mobile/Web Client: React with Google Maps integration
- Real-time Layer: Socket.IO server with Redis adapter for horizontal scaling
- API Layer: Express.js REST API for bookings, payments, history
- Database: MongoDB with geospatial indexes for location queries
- Cache: Redis for driver locations and session management
Key Features
Results & Impact
Average pickup time reduced to under 8 minutes
Real-time updates with less than 500ms latency
Successfully handled 1000+ concurrent connections in load testing
Zero data loss during network interruptions (offline queue)
What I Learned
Geospatial indexing is crucial - queries went from 2s to 20ms
WebSocket connection management is harder than it looks
Battery optimization on mobile requires careful throttling
Redis pub/sub is perfect for real-time routing
What's Next
Interested in working together?
I'm always open to discussing new projects and opportunities.