$ ./taxicarpooling --platform

> Intelligent carpooling platform with ML-powered matching algorithms

project-overview.sh
hongzhi@dev:~/projects$ cat project_info.txt
🚖 TAXICARPOOLING PLATFORM - GOTHENBURG SMART MOBILITY
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📍 Location: Gothenburg, Sweden
🎯 Purpose: Optimize taxi sharing with intelligent matching
⚡ Tech Stack: Spring Boot + React Native + PostgreSQL
🔥 Features: Real-time matching, payment integration, ML algorithms

// System Architecture

React Native
Mobile App
→
Spring Boot
REST API
→
PostgreSQL
Database
→
Google Maps
Geolocation
// Core matching algorithm
@Service
public class MatchingAlgorithm {
    
    @Autowired
    private LocationService locationService;
    
    public List<RideMatch> findOptimalMatches(RideRequest request) {
        // Calculate distance matrix between pickup points
        Matrix distanceMatrix = locationService.calculateDistances(
            request.getPickupLocation(), 
            getNearbyRequests(request)
        );
        
        // Apply ML algorithm for optimal grouping
        return mlEngine.optimizeRoutes(distanceMatrix, request);
    }
}
               

Backend Engineering

  • Spring Boot 2.7.x
  • Spring Security + JWT
  • JPA/Hibernate ORM
  • RESTful API Design
  • Microservices Architecture

Frontend Development

  • React Native 0.70+
  • Redux State Management
  • React Navigation 6.x
  • Async Storage
  • Push Notifications

Database & APIs

  • PostgreSQL 14.x
  • Google Maps API
  • Stripe Payment API
  • Firebase Cloud Messaging
  • Redis Caching

DevOps & Tools

  • Docker Containerization
  • GitHub Actions CI/CD
  • AWS EC2 Deployment
  • Nginx Load Balancer
  • Postman API Testing

// Core Features

🧠 Smart Matching Algorithm

Machine learning-powered algorithm that analyzes pickup/dropoff locations, time preferences, and user ratings to create optimal carpooling groups. Reduces wait times by 40% compared to traditional taxi services.

📍 Real-time Geolocation

Integration with Google Maps API for precise location tracking, route optimization, and ETA calculations. Live tracking allows users to monitor their ride progress in real-time.

💳 Integrated Payment System

Seamless payment processing through Stripe API with automatic fare splitting among carpooling participants. Supports multiple payment methods and provides detailed billing history.

🔔 Push Notifications

Real-time notifications via Firebase Cloud Messaging for ride confirmations, driver arrivals, route changes, and payment confirmations. Keeps users informed throughout their journey.

⭐ Rating & Review System

Comprehensive rating system for both passengers and drivers, with detailed feedback mechanisms to maintain service quality and build trust within the community.

📊 Analytics Dashboard

Admin dashboard with real-time analytics, user metrics, ride statistics, and financial reports. Provides insights for business optimization and user experience improvements.

// Implementation Highlights

deployment-stats.log
[INFO] Deployment completed successfully
[STATS] Backend API: 47 endpoints implemented
[STATS] Database: 12 optimized tables with indexing
[STATS] Mobile App: 23 screens with navigation
[STATS] Test Coverage: 85% (Unit + Integration)
[PERF] API Response Time: < 200ms average
[PERF] Algorithm Efficiency: O(n log n) matching complexity
// Advanced matching algorithm with ML optimization
@Component
public class MLMatchingEngine {
    
    public List<OptimalGroup> findOptimalGrouping(
        List<RideRequest> requests, 
        LocationMatrix matrix
    ) {
        // Apply K-means clustering for geographical grouping
        KMeansResult clusters = kMeansAlgorithm.cluster(
            requests.stream()
                .map(r -> new GeoPoint(r.getLat(), r.getLng()))
                .collect(toList())
        );
        
        // Optimize routes within each cluster
        return clusters.getClusters()
            .parallelStream()
            .map(cluster -> routeOptimizer.optimize(cluster))
            .collect(toList());
    }
}