Comparative Analysis of Some Methods and Algorithms for Traffic Optimization in Urban Environments Based on Maximum Flow and Deep Reinforcement Learning

This paper presents a comparative analysis between classical maximum flow algorithms and modern deep Reinforcement Learning (RL) algorithms applied to traffic optimization in urban environments. Through SUMO simulations and statistical tests, algorithms such as Ford–Fulkerson, Edmonds–Karp, Dinitz, Preflow–Push, Boykov–Kolmogorov and Double D Q N are compared. Their efficiency and stability are evaluated in terms of metrics such as cumulative vehicle dispersion and the ratio of waiting time to vehicle number. The results show that classical algorithms such as Edmonds–Karp and Dinitz perform stably under deterministic conditions, while Double D Q N suffers from high variation. Recommendations are made regarding the selection of an appropriate algorithm based on the characteristics of the environment, and opportunities for improvement using DRL techniques such as PPO and A2C are indicated.