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Smart city article details
| Title | Dynamic Traffic Flow Optimization Using Reinforcement Learning And Predictive Analytics: A Sustainable Approach To Improving Urban Mobility In The City Of Belgrade |
|---|---|
| ID_Doc | 21429 |
| Authors | Skoropad V.N.; Deđanski S.; Pantović V.; Injac Z.; Vujičić S.; Jovanović-Milenković M.; Jevtić B.; Lukić-Vujadinović V.; Vidojević D.; Bodolo I. |
| Year | 2025 |
| Published | Sustainability (Switzerland), 17, 8 |
| DOI | http://dx.doi.org/10.3390/su17083383 |
| Abstract | Efficient traffic management in urban areas represents a key challenge for modern cities, particularly in the context of sustainable development and reducing negative environmental impacts. This paper explores the application of artificial intelligence (AI) in optimizing urban traffic through a combination of reinforcement learning (RL) and predictive analytics. The focus is on simulating the traffic network in Belgrade (Serbia, Europe), where RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, are used for dynamic traffic signal control. The model optimized traffic signal operations at intersections with high traffic volumes using real-time data from IoT sensors, computer vision-enabled cameras, third-party mobile usage data and connected vehicles. In addition, implemented predictive analytics leverage time series models (LSTM, ARIMA) and graph neural networks (GNNs) to anticipate traffic congestion and bottlenecks, enabling initiative-taking decision-making. Special attention is given to challenges such as data transmission delays, system scalability, and ethical implications, with proposed solutions including edge computing and distributed RL models. Results of the simulation demonstrate significant advantages of AI application in 370 traffic signal control devices installed in fixed timing systems and adaptive timing signal systems, including an average reduction in waiting times by 33%, resulting in a 16% decrease in greenhouse gas emissions and improved safety in intersections (measured by an average reduction in the number of traffic accidents). A limitation of this paper is that it does not offer a simulation of the system’s adaptability to temporary traffic surges during mass events or severe weather conditions. The key finding is that integrating AI into an urban traffic network that consists of fixed-timing traffic lights represents a sustainable approach to improving urban quality of life in large cities like Belgrade and achieving smart city objectives. © 2025 by the authors. |
| Author Keywords | AI; mobility; optimization; sustainability |
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