Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of Belgrade

Volodymyr N. Skoropad (), Stevica Deđanski, Vladan Pantović, Zoran Injac, Slađana Vujičić, Marina Jovanović-Milenković, Boris Jevtić, Violeta Lukić-Vujadinović, Dejan Vidojević and Ištvan Bodolo
Additional contact information
Volodymyr N. Skoropad: Faculty of Business and Law, University Milija Babović—MB, 11000 Belgrade, Serbia
Stevica Deđanski: Faculty for Social Sciences, University Business Academy, 21107 Novi Sad, Serbia
Vladan Pantović: Faculty of Information Technology and Engineering, University Union–Nikola Tesla, 11158 Belgrade, Serbia
Zoran Injac: Faculty for Traffic Engineering, Pan Apeiron University, 78102 Banja Luka, Bosnia and Herzegovina
Slađana Vujičić: Faculty of Business Economics and Entrepreneurship, 11158 Belgrade, Serbia
Marina Jovanović-Milenković: Project Management College, Educons University, 11158 Belgrade, Serbia
Boris Jevtić: Computing Faculty (Racunarski Fakultet—RAF), University Union Belgrade, 11000 Belgrade, Serbia
Violeta Lukić-Vujadinović: Department for Industrial Engineering, Faculty of Engineering Management and Economics, University Business Academy Novi Sad, 21000 Novi Sad, Serbia
Dejan Vidojević: Department for Criminalistics, University of Criminal Investigation and Police Studies, 11158 Belgrade, Serbia
Ištvan Bodolo: Department for Industrial Engineering, Faculty of Engineering Management and Economics, University Business Academy Novi Sad, 21000 Novi Sad, Serbia

Sustainability, 2025, vol. 17, issue 8, 1-31

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.

Keywords: mobility; AI; optimization; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/8/3383/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/8/3383/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:8:p:3383-:d:1632094

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().