MODERN METHODS OF ROAD TRAFFIC MANAGEMENT IN CITIES

Shepelev, Vladimir; Almetova, Zlata; Mikhalchuk, Veronika; Slobodin, Ivan · 2019 · Crossref

DOI: 10.14529/em190419

archive: archived pipeline: cataloged verified

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Summary

This review paper addresses the challenge of urban traffic congestion caused by increasing motorization and inefficient traffic signal timing. The authors argue that while architectural solutions are often capital-intensive or impractical, optimizing traffic light synchronization through "Green Wave" systems offers an effective alternative. The primary goal of such systems is to coordinate signal phases across multiple intersections to minimize vehicle stops, reduce travel time, and improve safety. The paper provides a comprehensive overview of eight distinct methods for calculating and optimizing these coordinated traffic flows, categorizing them into classical mathematical models, cyclically expanded networks, MATLAB-based simulations, multi-agent systems, neural networks, and graph-analytical techniques. The analysis details several specific approaches. The classical model uses autonomous optimization to minimize total travel time but is limited by its inability to account for multi-objective performance or phase sequences. Cyclically expanded networks offer a more practical application by simultaneously optimizing signal timing and vehicle distribution. MATLAB-based strategies focus on bidirectional green waves to eliminate stops for opposing traffic flows. More advanced methods include adaptive systems utilizing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, which incorporate real-time data on weather, vehicle types, and driver behavior. Multi-agent systems, such as the HUTSIG model, combine real-time simulation with fuzzy logic to manage decentralized control; one such system demonstrated a 42.76% reduction in average delay compared to fixed-time signals. Additionally, neural network modeling is presented as a tool to predict optimal signal delays based on input parameters like intersection distance and flow speed. Finally, the paper discusses a graph-analytical method for designing green waves, noting its high labor intensity but utility for roads with at least two lanes. The findings indicate that adaptive and intelligent systems significantly outperform traditional fixed-cycle models. The review highlights that coordinated management requires specific conditions, such as transit flow exceeding 70% and intersection spacing under 800 meters. The authors also identify that traffic flow speed in acceleration and deceleration zones varies based on road loading and lane count, necessitating dynamic speed adjustments for optimal green wave performance. The significance of this work lies in its synthesis of diverse optimization techniques, demonstrating that modern traffic management can reduce accident rates, increase road capacity, lower ecological impact, and improve driver comfort. The authors conclude that while classical models with common cycle times are less effective, adaptive systems leveraging multi-agent technologies, fuzzy logic, and real-time data integration represent the most promising direction for developing intelligent transport systems.

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• speed management