Applications of the Chaos Approach on Transportation Systems – A Review
DOI:
https://doi.org/10.56532/mjsat.v5i4.557Keywords:
Chaos Theory, Traffic Flow Prediction, Nonlinear Dynamics, Real-Time ForecastingAbstract
Chaos theory offers a robust analytical lens for interpreting the nonlinear and dynamic nature of transportation systems, particularly in relation to congestion management and incident propagation. This review consolidates global applications of chaos theory in traffic studies by examining its integration with classical mathematical models, machine learning techniques, and sensitivity analyses of complex traffic datasets. The methodology synthesizes findings from studies conducted in the United States, Slovenia, Germany, Iran, and China. For example, several studies reported prediction accuracy improvements of up to 15–25% when Lyapunov exponent-based features were combined with machine learning models. Chaos-based simulations also demonstrated a 30% reduction in noise sensitivity compared to conventional approaches, with observed Lyapunov exponents typically ranging from 0.1 to 0.5, indicating pronounced chaotic behaviour in short-term traffic dynamics. Despite these promising outcomes, practical challenges persist, particularly in embedding chaos-based models into real-time Intelligent Transportation Systems (ITS), due to noise interference and infrastructure constraints. The novelty of this paper lies in bridging theoretical foundations with empirical case studies to propose a conceptual framework for integrating chaos theory into real-time traffic forecasting systems, thereby offering actionable insights for adaptive, data-driven urban mobility management.
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