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

Silvia Baeva, Nikolay Hinov () and Plamen Nakov
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Silvia Baeva: Department of Mathematical Modelling and Numerical Methods, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 1000 Sofia, Bulgaria
Nikolay Hinov: Department of Computer Systems, Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Plamen Nakov: Department of Computer Systems, Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria

Mathematics, 2025, vol. 13, issue 14, 1-40

Abstract: 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.

Keywords: traffic optimization; maximum flow; deep reinforcement learning; urban environment; intelligent transportation systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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