Urban Traffic Signal Optimization Using an Enhanced Genetic Algorithm

Employing an enhanced genetic algorithm for urban traffic signal optimization proves to be a highly effective methodology. This approach facilitates improved traffic flow management, reduction of congestion, and enhanced transportation efficiency. The accompanying complete codebase demonstrates key implementation aspects including chromosome encoding for signal timing parameters, adaptive mutation operators, and fitness functions evaluating traffic delay metrics. Through code analysis, you can observe practical applications of selection mechanisms (tournament/roulette wheel), crossover techniques (single-point/two-point), and constraint handling for real-world traffic optimization scenarios. The implementation provides insights into algorithmic principles and their applicability in intelligent transportation systems, supporting further research or project development in this domain. We anticipate this code will serve as a valuable resource for your technical explorations!