Assessing the Traffic Capacity of Urban Road Intersections

Shepelev, Vladimir; Glushkov, Alexandr; Gritsenko, Alexandr; Nevolin, Dmitry; Vorobyev, Andrey · 2022 · Crossref

DOI: 10.3389/fbuil.2022.968846

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Summary

This study addresses the challenge of managing traffic congestion in rapidly urbanizing areas by developing methods to assess the traffic capacity of urban road intersections. The authors aim to create a foundation for a dynamic monitoring system that generates real-time maps of traffic flow intensity, enabling municipal authorities to prevent peak loads and reduce traffic-related emissions. The research focuses specifically on through traffic lanes at regulated intersections, accounting for both the geometric uniqueness of intersections and the variable nature of vehicle movement, such as passing freely versus accelerating from a stop. The methodology involved collecting empirical data from ten major urban intersections in Chelyabinsk, Russia, using stationary street cameras. The researchers analyzed 24 traffic lanes, recording 480 measurements of passenger vehicles. They defined initial variables including intersection geometry (distance from stop line to exit), travel times for free and stopped vehicles, speed, and acceleration. Secondary variables, such as saturation flow and traffic capacity, were calculated using formulas from the Highway Capacity Manual, adjusted for factors like lane width, vehicle type, and turning interference. The data were processed using SPSS software to perform multiple regression analysis and correlation analysis. Additionally, the authors employed fuzzy logic methods, specifically E. Mamdani’s algorithm, to predict traffic capacity while accounting for unpredictable random factors like weather conditions. The results yielded a statistically significant multiple regression model explaining 74.2% of the variance in traffic lane capacity. The analysis identified saturation flow and the time required to pass through an intersection without stopping as the most influential variables on capacity. Conversely, vehicle speed and acceleration after a stop showed statistically insignificant impacts. The fuzzy logic model, which utilized Gaussian membership functions for variables including intersection length, travel time, and saturation flow, successfully predicted traffic capacity across varying conditions. The modeling demonstrated that saturation flow has a determining impact on capacity, while intersection geometry and travel time also significantly influence throughput. The significance of this work lies in providing a reliable mathematical framework for estimating the maximum traffic capacity of urban intersections. By integrating regression analysis with fuzzy logic, the study offers a tool for predicting traffic loads and identifying weak sections of road networks. These models support informed decision-making for traffic signal synchronization and network management, ultimately aiding in the reduction of congestion and environmental pollution. The authors note that future research will expand this approach to include all traffic lanes, including turn lanes, to estimate the capacity of entire intersections.

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• traffic density