Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Vladimir Shepelev (), Aleksandr Glushkov, Ivan Slobodin and Mohammed Balfaqih
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Vladimir Shepelev: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Aleksandr Glushkov: Department of Mathematical and Computer Modeling, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Ivan Slobodin: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Mohammed Balfaqih: Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia

Mathematics, 2023, vol. 11, issue 16, 1-30

Abstract: This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

Keywords: vehicle queue structure; pollutant emissions; fuzzy logic method; intelligent vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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