Modeling of traffic-light signalization depending on the quality of traffic flow in the city

Novikov, Alexander; Novikov, Ivan; Shevtsova, Anastasia · 2019 · Crossref

DOI: 10.5937/jaes17-18117

archive: archived pipeline: cataloged verified

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Summary

This study addresses the inefficiency of urban traffic light signalization, which often fails to account for the changing qualitative composition of traffic flows. High motorization rates have led to congestion, yet traditional traffic management methods typically treat all vehicles as a homogeneous group, ignoring variations in vehicle size and performance. The authors argue that this oversight reduces the effectiveness of traffic control, leading to increased delays and queue lengths. To resolve this, the paper proposes a refined methodology for calculating traffic signal cycles that incorporates the specific distribution of vehicle types within the traffic stream. The methodology involves classifying passenger cars into six categories (A–F) based on overall length, following Western European standards. The authors conducted field studies in Belgorod, Russia, to determine the "presence coefficients" for each vehicle class, representing their share in the total traffic flow. These coefficients were integrated into a mathematical model to calculate the saturation flow and optimal cycle time for traffic lights. To validate the model, the authors performed a computer simulation using AIMSUN software on a busy four-way intersection in Belgorod during peak hours (12:00–13:00). The simulation compared two scenarios: one using standard calculation methods that ignore vehicle composition and another using the proposed composition-aware model. The results demonstrated significant improvements in traffic efficiency when accounting for vehicle composition. The simulation revealed that the proposed method reduced the average queue length by 12% compared to the standard method. Additionally, the average delay per stopped vehicle decreased by 9%. Specifically, the optimized signal cycle duration was adjusted from 78 seconds to 90 seconds, with phase durations recalibrated to better match the actual traffic dynamics. The saturation flow values for each incoming direction were also recalculated, showing variations that justified the need for composition-specific adjustments. The study concludes that integrating the qualitative composition of traffic flows into signalization calculations enhances the performance of urban traffic management systems. By adapting signal timing to the specific mix of vehicle sizes present, cities can reduce congestion and improve throughput without requiring costly infrastructure changes. The authors recommend further field experiments to verify the convergence between simulation results and real-world performance, suggesting that this approach offers a cost-effective, conservative solution to urban traffic problems.

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• traffic density