Measures to ensure road traffic safety on controlled intersections

Novikov, Aleksandr; Novikov, Ivan; Shevtsova, Anastasiya · 2019 · Crossref

DOI: 10.1051/matecconf/201929800046

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Summary

This paper addresses the challenge of improving road traffic safety and efficiency at controlled intersections, specifically within the context of Intelligent Transport Systems (ITS). The authors argue that traditional traffic light control methods fail to account for dynamic variables such as the heterogeneity of vehicle fleets and changing weather conditions. Motivated by the need to integrate adaptive control mechanisms into existing infrastructure, the study aims to develop a calculation method for traffic light cycles that adjusts for vehicle composition and environmental factors to reduce accidents, delays, and fuel consumption. The methodology involves a two-part analysis focusing on traffic flow composition and weather impacts. First, the authors analyzed vehicle fleet data from the Russian Federation, noting a doubling of vehicles between 2002 and 2018. They categorized passenger cars into six classes (A–F) based on length using Western European standards and introduced a "factor of presence" coefficient to quantify the diversity of vehicle types in traffic streams. Experimental data from the regional centers of Belgorod and Orel established specific presence coefficients for these classes. Second, the study examined the influence of weather on traffic safety by modeling the relationship between ambient air temperature and asphalt pavement temperature. This analysis determined the coefficient of adhesion for various temperature ranges, which directly affects braking distances and saturation flow rates. These parameters were integrated into an improved algorithm for calculating traffic light phase durations and cycle lengths. To validate the proposed approach, the authors conducted a computer simulation using TRANSYT-14 TRL software. The model simulated a signaled intersection in a regional network, comparing the performance of the existing traffic light regime against the proposed adaptive regime during the peak hour of 08:00–09:00. The results demonstrated that the new method improved all key performance indicators by an average of 12%. Specifically, stops and delays decreased from 260.99 to 210.99, queues and congestion reduced from 134.29 to 121.02, and fuel consumption dropped from 130.34 to 109.15 units. The significance of this research lies in its contribution to the development of adaptive traffic control systems within ITS. By accounting for vehicle heterogeneity and weather-dependent adhesion coefficients, the proposed method allows for more rational distribution of traffic light time. The authors conclude that implementing this approach reduces unintended vehicle stops, alleviates congestion, and lowers the accident rate at regulated intersections. Furthermore, the reduction in fuel consumption highlights the environmental benefits of the system. The study confirms that integrating such dynamic monitoring and calculation methods into ITS is a feasible and effective strategy for enhancing urban transport safety and efficiency.

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• roadway lighting effects