Fractal Characteristics of Mountain Cities' Traffic Flow Based on EMD and Multifractal

Zhao, Ling; Cao, Juan; Mi, Bo

doi:10.4028/www.scientific.net/amr.831.430

Cited by 1 publication

(1 citation statement)

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“…Specifically, the health management of an urban transportation system, which is a typical critical infrastructure, has accompanying complexities. Zhao et al (2012) analyzed the traffic flow data of mountainous cities and proposed a method for measuring the subhealth state of a system based on the fractal characteristics of city traffic. Chen et al (2015) proposed a set of health indices for evaluating the health status of a bus line network in a typical metropolitan city.…”

Section: System Healthmentioning

confidence: 99%

Percolation-based health management of complex traffic systems

et al. 2021

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System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio-temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.

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Section: System Healthmentioning

confidence: 99%