Quantification of chaos in a time series generated from a traffic flow model using the extended entropic chaos degree

Kei Inoue and Kazuki Tani

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: Several Traffic flow models have been proposed to address the underlying causes of traffic congestion and develop effective methods for its suppression. Typically, these models are represented using differential or difference equations. Traffic flow models can exhibit chaotic behaviors. Traffic situations become unstable if chaos prevails in the traffic flow. Therefore, the conditions under which chaos occurs in the traffic flow and the intensity of chaos at that time must be investigated. The Lyapunov exponent is used for quantifying the chaos in the dynamical systems. However, computing the Lyapunov exponent over time series without a dynamical map is challenging. Recently, the extended entropic chaos degree has been introduced as an extension of the entropic chaos degree. The extended entropic chaos degree can be computed directly for any time series. Analytically, the extended entropic chaos degree equals the sum of all the Lyapunov exponents for multidimensional non-periodic maps. Moreover, the extended entropic chaos degree is mathematically shown to equal the sum of one positive and one negative Lyapunov exponents for two-dimensional typical chaotic maps, such as a generalized Baker’s map, and a standard map.

Keywords: Chaos; Lyapunov exponent; Extended entropic chaos degree; Traffic flow model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010524

DOI: 10.1016/j.chaos.2023.114150

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