Real-Time Parking Optimization System
Advanced algorithms for urban parking challenges using game theory, A* pathfinding, ML forecasting, and driver psychology modeling
Introduction
This comprehensive parking optimization system addresses critical urban mobility challenges through advanced algorithmic solutions. Developed for CIS 505 (Algorithms Analysis and Design) at the University of Michigan - Dearborn, the system demonstrates practical applications of complex algorithms in real-world scenarios.
Urban parking inefficiency costs cities billions annually. Drivers spend an average of 17 minutes searching for parking, creating traffic congestion, wasted fuel, and reduced air quality. Our system tackles this challenge through five interconnected algorithmic approaches:
- Dynamic Pricing: Game theory-based Nash equilibrium optimization
- Smart Routing: A* pathfinding with real-time traffic integration
- Demand Prediction: Machine learning forecasting with historical analysis
- City Coordination: Distributed optimization using divide-and-conquer
- Driver Psychology: Behavioral modeling with 6 personality types
Core Algorithms
The system integrates multiple advanced algorithms, each mathematically proven and complexity-analyzed for academic rigor:
Dynamic Pricing Algorithm
Uses game theory to optimize pricing across competing zones, finding Nash equilibrium points that maximize both revenue and utilization.
A* Route Optimization
Implements A* pathfinding with real-time traffic integration, providing optimal routes while considering current congestion patterns.
ML Demand Prediction
Uses dynamic programming with machine learning to forecast parking demand patterns based on historical data and real-time events.
Real-World Data Integration
The system validates its algorithms using real Grand Rapids, Michigan downtown data, demonstrating practical applicability:
Geographic Analysis
Network Infrastructure
API Integration
- TomTom API: Primary traffic data provider (2,500 calls/day free)
- Google Maps API: Alternative with $200/month credit
- Mapbox API: Backup service with 100k requests/month
- Offline Mode: 100% functional without external APIs
City Simulation Engine
Advanced simulation environment with realistic driver behavior modeling and city-scale optimization:
Driver Psychology Model
Six distinct personality types with realistic decision-making patterns:
- Optimizer: Minimizes total cost (time + money)
- Satisficer: Chooses first acceptable option
- Risk-averse: Prefers guaranteed availability
- Impatient: Prioritizes shortest walking distance
- Budget-conscious: Minimizes monetary cost
- Explorer: Willing to try new parking areas
Simulation Parameters
Performance Results
Comprehensive evaluation demonstrates significant improvements in parking efficiency and revenue optimization:
Revenue Analysis
Algorithm Complexity Validation
Mathematical verification of theoretical complexity bounds:
| Algorithm | Time Complexity | Space Complexity | Verification Status |
|---|---|---|---|
| A* Routing | O((V+E) log V) | O(V+E) | ✅ Proven |
| Dynamic Pricing | O(z²) | O(z) | ✅ Benchmarked |
| Demand Prediction | O(t×s²) | O(t×s) | ✅ Validated |
| City Coordination | O(z²/d + d²) | O(z) | ✅ Measured |
Key Performance Indicators
Quick Start Guide
Get the parking optimization system running in minutes:
Installation
# Clone repositorygit clone
https://github.com/jeremy-cleland/parking-optimizationcd parking_optimization# Setup environmentmake setup# Run complete demomake run
Available Commands
| Command | Description |
|---|---|
make run |
Complete simulation with analysis and visualization |
make simulate |
City simulation only |
make test |
Run comprehensive test suite |
make show-run |
Display latest simulation results |
Interactive Visualization
Explore the interactive parking map to see real-time optimization in action.
Academic Team
Course: CIS 505 Algorithms Analysis and
Design
Institution: University of Michigan -
Dearborn
Term: Summer 2025
This project demonstrates practical application of advanced algorithms in real-world urban planning scenarios, with mathematical validation and complexity analysis suitable for academic evaluation.