Daniela, Koltovska Nechoska and Renata, Petrevska Nechkoska and Renata, Duma and Mimoza, Bogdanovska Jovanovska (2025) AI-based traffic control and management systems - a synthesis of Macedonian research experiences. Transportation Research Procedia, 83. pp. 513-519.
Full text not available from this repository.Abstract
Under of influence of advances in Artificial Intelligence and Machine Learning technology, the transport sector is on the brink of
a major revolution. Algorithms from these areas have been implemented in various fields, from autonomous vehicles, and
innovative tools for gathering and visualizing traffic data for in-depth analysis of traffic flow characteristics, to the next generation
of intelligent traffic control and management systems. Through two case studies, this paper aims to present the synthesis of
Macedonian research experience gained over a long process of exploring the opportunities and challenges of development and
integrating emerging technologies into the transport system. The first case study describes the implementation of a reinforcement
learning (RL) algorithm in the design and testing of an adaptive control strategy at urban intersections. The second case study
involves the application of a sophisticated AI-based tool for traffic flow data gathering, by video data processing. In both cases,
the process of testing and evaluation is obtained in the simulation environment. The obtained simulation results demonstratedthe
efficiency of the implemented solutions in decreasing traffic congestion, and vehicle emissions, and increasing throughputs which
benefits society as a whole
| Item Type: | Article |
|---|---|
| Subjects: | Scientific Fields (Frascati) > Engineering and Technology > Other engineering and technologies |
| Divisions: | Faculty of Technical Sciences |
| Depositing User: | Prof. d-r. Daniela Koltovska Nechoska |
| Date Deposited: | 25 Mar 2025 09:12 |
| Last Modified: | 25 Mar 2025 09:12 |
| URI: | https://eprints.uklo.edu.mk/id/eprint/10829 |
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