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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.

While abrupt regime shifts between different metastable states have occurred in natural systems from many areas including ecology, biology, and climate, evidence for this phenomenon in transportation systems has been rarely observed so far. This limitation might be rooted in the fact that we lack methods to identify and analyze possible multiple states that could emerge at scales of the entire traffic network. Here, using percolation approaches, we observe such a metastable regime in traffic systems. In particular, we find multiple metastable network states, corresponding to varying levels of traffic performance, which recur over different days. Based on high-resolution global positioning system (GPS) datasets of urban traffic in the megacities of Beijing and Shanghai (each with over 50,000 road segments), we find evidence supporting the existence of tipping points separating three regimes: a global functional regime and a metastable hysteresis-like regime, followed by a global collapsed regime. We can determine the intrinsic critical points where the metastable hysteresis-like regime begins and ends and show that these critical points are very similar across different days. Our findings provide a better understanding of traffic resilience patterns and could be useful for designing early warning signals for traffic resilience management and, potentially, other complex systems.

Background: Both Guillain–Barré syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) are neurodegenerative and inflammatory demyelination disorders. Sporadic reports showed that the increased levels of thyroid function and autoantibodies are associated with GBS, CIDP, or both, but no systematic study has been reported. We assessed the differences of thyroid function and autoantibodies between GBS and CIDP in a Chinese cohort.Methods: A total of 256 patients were enrolled in this study. 175 clinically confirmed GBS and CIDP patients were selected. Meanwhile, 81 patients hospitalized for diseases other than GBS or CIDP with mild symptoms were enrolled as a control group. Relevant clinical data, including thyroid function, and autoantibody examinations, were collected for statistical analysis.Results: In the comparison of thyroid function and autoantibody parameters, the levels of total thyroxine (TT4), thyroid peroxidase antibody (TPO-Ab), and thyroglobulin antibody (TG-Ab) in the GBS group were all higher than those in the CIDP and Control groups (P < 0.01). The thyroid antibody positive rates in the GBS and CIDP groups were 70.10 and 14.10%, respectively (P < 0.01). In the receiver operating characteristic (ROC) curve analysis, TT4, TPO-Ab, and TG-Ab were higher in the GBS group and lower in the CIDP group (P < 0.01). To achieve a high specificity of 97–99%, the diagnostic cutoff value of TPO-Ab was higher than 133 IU/mL (Sensitivity: 11.34%) or lower than 0.01 IU/mL (Sensitivity: 9.09%), while the diagnostic cutoff value of TG-Ab was higher than 261.1 IU/mL (Sensitivity: 2.06%) or lower than 0.46 IU/mL (Sensitivity: 11.69%). Multivariate logistic regression analysis showed that the differences in TPO-Ab were statistically significant between GBS patients with TPO-Ab was higher than 133 IU/mL and CIDP patients (P < 0.01); the differences in TG-Ab were statistically significant between GBS patients with TG-Ab was higher than 261.1 IU/mL and CIDP patients (P < 0.05).Conclusion: The elevation of thyroid autoantibodies was associated with GBS. TPO-Ab higher than 133 IU/mL or lower than 0.01 IU/mL and TG-Ab higher than 261.1 IU/mL or lower than 0.46 IU/mL had high specificity for differentiating between GBS and CIDP; therefore, TPO-Ab and TG-Ab can be used as biomarkers for the differential diagnosis of GBS and CIDP.

Extreme weather, such as torrential rain, could lead to severe damage to transportation. Many studies have been proposed focusing on the influence of extreme weather on the traffic flow properties. However, the robustness of whole dynamic traffic networks under extreme weather is rarely addressed. Particularly, little attention has been paid to the question whether and how the local destruction of roads is aggregated into a degradation of global traffic operation. Based on real-time traffic data, here we apply percolation analysis on traffic networks and find that the torrential rain can lead to different effects on different levels: on the network scale, the traffic percolation threshold as an indicator for city traffic reliability is stable against weather perturbation, while a portion of roads at the microscopic level is significantly influenced and forming local cluster isolated from the main functional network. This may be due to the fact that torrential rain and other extreme weather condition will not only generate the damaged roads in the supply end, but also reduce the traffic demand correspondingly. Our research suggests the traffic percolation may reflect the nature of relation between local flow and global flow, which can help to design corresponding management strategies.

The concept of resilience can be realized in natural and engineering systems, representing the ability of system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation system, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations is still missing. Here we define city traffic resilience based on the spatio-temporal clusters of congestion in real traffic, and find that the resilience follows a scale free distribution in two-dimensional city road networks and one-dimensional highways, with different exponents, but similar exponents in different days and different cities. The traffic resilience is also revealed to have a novel scaling relation between the cluster size of the spatio-temporal jam and its recovery duration, independent of microscopic details. Our findings of universal traffic resilience can provide indication towards better understanding and designing these complex engineering systems under internal and external disturbances., Comment: 6 pages, 4 figures

Percolation transition is widely observed in networks ranging from biology to engineering. While much attention has been paid to network topologies, studies rarely focus on critical percolation phenomena driven by network dynamics. Using extensive real data, we study the critical percolation properties in city traffic dynamics. Our results suggest that two modes of different critical percolation behaviors are switching in the same network topology under different traffic dynamics. One mode of city traffic (during nonrush hours or days off) has similar critical percolation characteristics as small world networks, while the other mode (during rush hours on working days) tends to behave as a 2D lattice. This switching behavior can be understood by the fact that the high-speed urban roads during nonrush hours or days off (that are congested during rush hours) represent effective long-range connections, like in small world networks. Our results might be useful for understanding and improving traffic resilience., 8 pages, 4 figures, Daqing Li, Ziyou Gao and H. Eugene Stanley are the corresponding authors (daqingl@buaa.edu.cn, zygao@bjtu.edu.cn, hes@bu.edu)