{"id":29583794,"url":"https://github.com/jeremy-cleland/parking_optimization","last_synced_at":"2025-07-19T23:38:59.933Z","repository":{"id":298436754,"uuid":"999870775","full_name":"Jeremy-Cleland/parking_optimization","owner":"Jeremy-Cleland","description":"Real-time collaborative parking optimization system using advanced algorithms including game theory Nash equilibrium, A* pathfinding, ML forecasting, and driver psychology modeling. 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Dearborn.\n\n## Overview\n\nThis system solves urban parking challenges using advanced algorithms including:\n\n- **Dynamic Pricing**: Game theory Nash equilibrium optimization\n- **Smart Routing**: A* pathfinding with real-time traffic integration\n- **Demand Prediction**: ML-based forecasting with historical analysis\n- **City Coordination**: Distributed optimization using divide-and-conquer\n- **Driver Psychology**: Behavioral modeling with 6 personality types\n\n## Quick Start\n\n```bash\n# Install dependencies\nmake setup\n\n# Run complete demo\nmake run\n\n# Run simulation only\nmake simulate\n\n# View results\nmake show-run\n```\n\n## Project Structure\n\n```text\nparking_optimization/\n├── core/                    # Core algorithm implementations\n│   ├── parking_zone.py      # Zone management and occupancy\n│   ├── dynamic_pricing.py   # Game theory pricing optimization\n│   ├── route_optimizer.py   # A* routing with traffic integration\n│   ├── demand_predictor.py  # ML-based demand forecasting\n│   ├── coordinator.py       # City-wide coordination algorithms\n│   ├── traffic_manager.py   # Real-time traffic API integration\n│   └── map_services/        # Map data and geographic services\n├── simulation/              # City simulation environment\n│   ├── city_simulator.py    # Main simulation engine\n│   └── driver_behavior.py   # Psychological driver modeling\n├── analysis/                # Performance analysis and visualization\n│   ├── visualizer.py        # Chart generation and dashboards\n│   └── complexity_analysis.py # Algorithm complexity validation\n├── tests/                   # Comprehensive test suite\n├── scripts/                 # Utility scripts and tools\n├── docs/                    # Documentation\n└── output/                  # Generated results and visualizations\n```\n\n## Features\n\n### Core Algorithms\n\n- **Dynamic Pricing**: O(z²) complexity, Nash equilibrium optimization\n- **Route Optimization**: O((V + E) log V) A* pathfinding with traffic\n- **Demand Prediction**: O(t × s²) dynamic programming forecasting\n- **City Coordination**: O(z²/d + d²) divide-and-conquer optimization\n\n### Real-World Integration\n\n- Google Maps API integration for traffic data\n- Mapbox API support with 100k free requests/month\n- Fallback mode works completely offline\n- Grand Rapids, MI real-world data validation\n\n### Driver Psychology\n\nSix personality types with realistic behaviors:\n\n- Optimizer, Satisficer, Risk-averse, Impatient, Budget-conscious, Explorer\n\n## Available Commands\n\n```bash\n# Execution\nmake run              # Run simulation demo\nmake simulate         # Run city simulation\nmake report           # Generate analysis and visualization report\n\n# Testing\nmake test             # Run all tests\nmake test-coverage    # Run tests with coverage\n\n# Run Management\nmake list-runs        # List all simulation runs\nmake show-run         # Show latest run details\nmake cleanup-runs     # Clean up old runs\n\n# Setup \u0026 Maintenance\nmake setup            # Set up environment and dependencies\nmake deps             # Update dependencies\nmake clean            # Clean temporary files\nmake help             # Show all commands\n```\n\n## Results\n\nThe system demonstrates:\n\n- **Algorithmic optimization** with proven complexity analysis\n- **Dynamic pricing simulation** showing revenue optimization potential\n- **Real-time optimization** with \u003c100ms response times\n- **Scalable architecture** supporting 10,000+ concurrent users\n\n### 📊 Sample Visualizations\n\n#### Executive Dashboard\n\nComplete system overview with performance metrics and key insights:\n\n![Summary Dashboard](showcase/latest_run/selected_visuals/summary_dashboard.png)\n\n#### Real-World Geographic Analysis\n\nGrand Rapids downtown parking analysis with 113 zones and road network:\n\n![Geographic Dashboard](showcase/latest_run/selected_visuals/geographic_dashboard.png)\n\n#### Algorithm Performance Analysis\n\nComplexity analysis and system performance metrics:\n\n![Performance Metrics](showcase/latest_run/selected_visuals/performance_metrics.png)\n\n#### Network Infrastructure\n\nRoad network analysis showing 58 intersections and routing optimization:\n\n![Network Analysis](showcase/latest_run/selected_visuals/network_analysis_map.png)\n\n*Interactive map available at: `showcase/latest_run/selected_visuals/interactive_parking_map.html`*\n\n## API Configuration (Optional)\n\nThe system works in 100% free mode without any API keys. For enhanced accuracy with real traffic data:\n\n1. **TomTom** (default): Get free key at [developer.tomtom.com](https://developer.tomtom.com/) - 2,500 calls/day free\n2. **Mapbox** (alternative): Get free token at [mapbox.com](https://account.mapbox.com/access-tokens/) - 100k calls/month free\n3. **Google Maps** (optional): Setup at [Google Cloud Console](https://console.cloud.google.com/) - requires credit card\n\n```bash\nexport TOMTOM_API_KEY=\"your_key_here\"\nexport MAPBOX_ACCESS_TOKEN=\"your_token_here\"\nexport GOOGLE_MAPS_API_KEY=\"your_key_here\"\n\n# Optional: Choose provider (defaults to tomtom)\nexport MAP_PROVIDER=\"tomtom\"  # or \"mapbox\" or \"google\"\n```\n\nSee [docs/API_SETUP_GUIDE.md](docs/API_SETUP_GUIDE.md) for detailed setup instructions.\n\n## Class\n\n**Course**: CIS 505 Algorithms Analysis and Design\n**Institution**: University of Michigan - Dearborn\n**Term**: Summer 2025\n\nThis project demonstrates practical application of advanced algorithms in real-world urban planning scenarios, with mathematical validation and complexity analysis.\n\n## Team\n\n- **Jeremy Cleland**\n- **Saif Khan**\n- **Asem Zahran**\n\n## Dependencies\n\n- Python 3.8+\n- NumPy, Pandas, SciPy for numerical computing\n- Matplotlib, Seaborn for visualization\n- Requests for API integration\n- Pydantic v2 for data validation\n\nComplete dependency list in `pyproject.toml` and `environment.yml`.\n\n## License\n\nMIT License - See LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremy-cleland%2Fparking_optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjeremy-cleland%2Fparking_optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremy-cleland%2Fparking_optimization/lists"}