Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions.

Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. Understanding the network dynamics of traffic bottlenecks can help avoid critical large traffic jams and improve overall traffic conditions. Here, we develop a method to forecast heavy congestions based on their early propagation stage. Our framework follows the network propagation and dissipation of the traffic jams originated from a bottleneck emergence, growth, and its recovery and disappearance. Based on large-scale urban traffic-speed data, we find that dissipation duration of jams follows approximately power-law distributions, and typically, traffic jams dissolve nearly twice slower than their growth. Importantly, we find that the growth speed, even at the first 15 minutes of a jam, is highly correlated with the maximal size of the jam. Our methodology can be applied in urban traffic control systems to forecast heavy traffic bottlenecks and prevent them before they propagate to large network congestions.

Differences in Thyroid Function and Autoantibodies in the Development of Guillain-Barré Syndrome vs. Chronic Inflammatory Demyelinating Polyradiculoneuropathy.

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.

Multiple metastable network states in urban traffic.

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.

Scale-free resilience of real traffic jams.

The concept of resilience can be realized in natural and engineering systems, representing the ability of a 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 systems, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations are still missing. Here, we define city traffic resilience based on the spatiotemporal clusters of congestion in real traffic and find that the resilience follows a scale-free distribution in 2D city road networks and 1D highways with different exponents but similar exponents on different days and in different cities. The traffic resilience is also revealed to have a scaling relation between the cluster size of the spatiotemporal jam and its recovery duration independent of microscopic details. Our findings of universal traffic resilience can provide an indication toward better understanding and designing of these complex engineering systems under internal and external disturbances.

Switch between critical percolation modes in city traffic dynamics.

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.