Congested Phase Research Articles

The presence of diabetes mellitus further impairs structural and functional capillary density in patients with chronic kidney disease

Endothelial dysfunction has been associated with increased cardiovascular events and overall mortality. Microvascular damage is prevalent both in diabetes mellitus (DM) and chronic kidney disease (CKD). Our aim was to compare microcirculatory function parameters in diabetic and non-diabetic CKD patients via nailfold video-capillaroscopy. We included 48 diabetic and 48 non-diabetic adult CKD patients. All participants underwent nailfold video-capillaroscopy, during which capillary density was measured at normal conditions (baseline), after a 4-minute arterial occlusion (postocclusive reactive hyperemia), and at the end of a 2-minute venous occlusion (congestion phase). Diabetic patients presented significantly lower capillary density during reactive hyperemia (36.3±3.8 vs 38.3±4.3 capillaries/mm2 , P=.022) and at venous congestion (37.8±4.0 vs 39.8±4.2 capillaries/mm2 , P=.015). When stratified according to CKD stages, only in stage 3b capillary density was significantly lower in diabetic compared to non-diabetic subjects at baseline, during postocclusive hyperemia (36.8±2.7 vs 40.0±4.3 capillaries/mm2 , P=.037) and venous congestion (38.3±2.8 vs 41.5±3.5 capillaries/mm2 , P=.022). Capillary density during postocclusive hyperemia and after venous congestion is lower in diabetic compared to non-diabetic CKD patients, a finding indicative that diabetes is an additional factor contributing to microcirculatory structural and functional impairment in CKD. These differences are more prominent in CKD stage 3b.

On the phase transition in the sublattice TASEP with stochastic blockage

We revisit the defect-induced nonequilibrium phase transition from a largely homogeneous free-flow phase to a phase-separated congested phase in the sublattice totally asymmetric simple exclusion process with local deterministic bulk dynamics and a stochastic defect that mimicks a random blockage. Exact results are obtained for the compressibility and density correlations for a stationary grandcanonical ensemble given by the matrix product ansatz. At the critical density the static compressibility diverges while in the phase separated state above the critical point the compressibility vanishes due to strong non-local correlations. These correlations arise from a long range effective interaction between particles that appears in the stationary state despite the locality of the microscopic dynamics.

Study on fuel consumption in the Kerner–Klenov–Wolf three-phase cellular automaton traffic flow model

Based on the Kerner–Klenov–Wolf three-phase cellular automaton traffic model, we study fuel consumption of vehicles on one-way lanes under open boundary conditions. The difference in the appearance of the phase diagram is found to be related to the length of the vehicle. Phase diagram is divided into free flow, congested phase I, congested phase II and maximum current (MC) phase for the 15 cells of vehicle length and lacks the maximum current (MC) phase for 5 cells of vehicle length. By analysis of the cross-correlation between local density and flow and the spatial–temporal patterns, synchronized flow (SF) on the area upstream of the road is determined. The numerical computations for fuel consumption on free flow, congested phase I, synchronous flow (SF) and maximum current (MC) are carried out. The results indicate that the injection rate and the removal rate have great impacts on fuel consumption in the open boundary traffic system. The maximum current (MC) phase and congested phase I maximize fuel consumption. The fuel consumption of synchronized flow (SF) exceeds that of the free flow, but lower than that of the congested phase I and the maximum current (MC).

P1012NAIL CAPILLARY DENSITY DURING POSTOCCLUSIVE REACTIVE HYPEREMIA AND VENOUS CONGESTION IS MORE IMPAIRED IN DIABETIC COMPARED TO NON-DIABETIC CKD PATIENTS

Abstract Background and Aims Αlterations in endothelial function and capillary circulation have been associated with increased cardiovascular events and overall mortality. Both diabetes mellitus (DM) and chronic kidney disease (CKD) have been associated with microcirculatory damage. Nailfold video-capillaroscory can provide a thorough assessment of capillary density and microcirculation changes. This is the first study examining in comparison microcirculatory function parameters in diabetic and non-diabetic patients with CKD. Method We included 48 diabetic and 48 non-diabetic adult patients (>18 years) with CKD (eGFR: <90 and ≥15mL/min/1.73m2), matched in a 1:1 ratio for age, sex and eGFR within each CKD stage (2, 3a, 3b and 4). All participants underwent nailfold video-capillaroscopy, during which capillary density was measured at normal conditions (baseline), after a 4-minute arterial occlusion (postocclusive reactive hyperemia) and at the end of a 2-minute venous occlusion (congestion phase). Results Baseline demographic, anthropometric and laboratory characteristics were similar between patients with and without diabetes in total and in CKD stages. Overall, no significant differences at baseline capillary density were observed between groups; however diabetic patients presented significantly lower capillary density during reactive hyperemia (36.3±3.8 vs 38.3±4.3 capillaries/mm2, p=0.022) and at venous congestion (37.8±4.0 vs 39.8±4.2 capillaries/mm2, p=0.015). When stratified according to CKD stages, the between-group differences in parameters of interest were not significant in stages 2, 3a and 4. In stage 3b, capillary density was significantly lower in diabetic compared to non-diabetic subjects at baseline (31.1±2.8 vs 33.4±3.4 capillaries/mm2, p=0.044), during postocclusive hyperemia (36.8±2.7 vs 40.0±4.3 capillaries/mm2, p=0.037) and venous congestion (38.3±2.8 vs 41.5±3.5 capillaries/mm2, p=0.022). Conclusion Capillary density during postocclusive reactive hyperemia and after venous congestion is lower in diabetic compared to non-diabetic CKD patients, a finding indicative that diabetes is an additional factor contributing to microcirculatory functional impairment in CKD. These differences are more prominent in CKD stage 3b, and less prominent in earlier and later stages.

Rare-event properties of the Nagel-Schreckenberg model.

We have studied the distribution of traffic flow q for the Nagel-Schreckenberg model by computer simulations. We applied a large-deviation approach, which allowed us to obtain the distribution P(q) over more than one hundred decades in probability, down to probabilities like 10^{-140}. This allowed us to characterize the flow distribution over a large range of the support and identify the characteristics of rare and even very rare traffic situations. We observe a change of the distribution shape when increasing the density of cars from the free flow to the congestion phase. Furthermore, we characterize typical and rare traffic situations by measuring correlations of q to other quantities like density of standing cars or number and size of traffic jams.

Dissipation of traffic congestion using autonomous-based car-following model with modified optimal velocity

We investigate dynamical properties of traffic flow using the stochastic car-following model with modified optimal velocity on circular road. The safety distance following the two-second rule and autonomous vehicles obeying simple requirements are incorporated into the model. The dynamic safety distance increases in a light traffic condition where the average driving velocity is high, while decreases in a dense traffic condition in anticipation of slower traffic motion. The results show that the presence of the autonomous vehicles can enhance overall velocity and traffic current of the system, and postpone the traffic congestion. In a particular phase region, imposing a speed limit enables the system to leave the congested flow phase. The density-dependent speed limit in autonomous-free condition is obtained to achieve the optimal traffic flow.

A behavioral microeconomic foundation for car-following models

The objective of this paper is to develop a micro-economic modeling approach for car-following behaviors that may capture different risk-taking tendencies when dealing with different traffic conditions. The proposed framework allows for the consideration of perception subjectivity and judgement errors that may lead to unsafe acceleration driving maneuvers with the possibility of real-end collisions. The modeling approach relies on a generalized utility-based formulation with three specific types of subjective utility functions (SUFs): Prospect Utility (PT) subjective utility function, Constant Relative Risk Aversion (CRRA) subjective utility function, and an Exponential Constant Relative Risk Aversion (ECRA) subjective utility function. The formulation is assessed in terms of its homogeneous macroscopic properties (thus leading to a triangular fundamental diagram) and its non-homogeneous microscopic properties (thus leading to realistic following behavior facing different traffic scenarios).Once tested in terms of feasibility, the modeling approach is calibrated against real-life trajectory data. A Genetic Algorithm (GA) method is adopted to minimize a spacing-mixed-error term while considering inter-driving heterogeneity. The three models (i.e. PT, CRRA and ECRA) produce acceptable error values with the PT model showing the best fit, followed by the ECRA model, followed by the CRRA model. Even though the CRRA and the PT models show similar intensity in the acceleration response to the behavior of a lead vehicle with more disturbance (stochasticity/randomness) seen with the CRRA SUF, the PT and the ECRA models show more realistic wave formation despite their difference in terms of individual acceleration distribution functions. The ECRA model results in an amplified sensitivity to the behavior of the lead vehicle with the acceleration probability distribution function skewed to the left (i.e. towards decelerating rather than accelerating).The calibration exercise is followed by a simulation exercise. The three suggested models produce a homogeneous congestion phase, but a clear transient single/multiple wave formation is seen with the PT and the ECRA models. These latter models are able to reproduce all the congestion regimes observed on real-world surface transportation networks. The ECRA is characterized by a decreased capacity and increased traffic disturbances with additional shockwave formation. Finally, the different models allow the possibility of perception or judgement errors with the explicit incorporation of a collision probability and a collision weight in the suggested formulation approach.

Congestion fronts of diffusing particles

We study two new models of two particle species invading a surface from opposite sides. Collisions of particles of different species lead to the formation of congestion fronts. One of the models implements a reversible process whereas in the other model the congestion front forms irreversibly. For both models we find that the congestion fronts are self-affine but with different roughness exponents. For low densities the system does not congest and we find a phase transition between a phase of freely moving particles and a congestion phase.

An existence result for a constrained two-phase transition model with metastable phase for vehicular traffic

In this paper we study a phase transition model for vehicular traffic flows. Two phases are taken into account, according to whether the traffic is light or heavy. We assume that the two phases have a non-empty intersection, the so called metastable phase. The model is given by the Lighthill–Whitham–Richards model in the free-flow phase and by the Aw–Rascle–Zhang model in the congested phase. In particular, we study the existence of solutions to Cauchy problems satisfying a local point constraint on the density flux. We prove that if the constraint F is higher than the minimal flux \(f_\mathrm{c}^-\) of the metastable phase, then constrained Cauchy problems with initial data of bounded total variation admit globally defined solutions. We also provide sufficient conditions on the initial data that guarantee the global existence of solutions also in the case \(F < f_\mathrm{c}^-\). These results are obtained by applying the wave-front tracking technique.

Renormalization-group study of the Nagel-Schreckenberg model.

We study the phase transition from free flow to congested phases in the Nagel-Schreckenberg (NS) model by using the dynamically driven renormalization group (DDRG). The breaking probability p that governs the driving strategy is investigated. For the deterministic case p=0, the dynamics remain invariant in each renormalization-group (RG) transformation. Two fully attractive fixed points, ρ_{c}^{*}=0 and 1, and one unstable fixed point, ρ_{c}^{*}=1/(v_{max}+1), are obtained. The critical exponent ν which is related to the correlation length is calculated for various v_{max}. The critical exponent appears to decrease weakly with v_{max} from ν=1.62 to the asymptotical value of 1.00. For the random case p>0, the transition rules in the coarse-grained scale are found to be different from the NS specification. To have a qualitative understanding of the effect of stochasticity, the case p→0 is studied with simulation, and the RG flow in the ρ-p plane is obtained. The fixed points p=0 and 1 that govern the driving strategy of the NS model are found. A short discussion on the extension of the DDRG method to the NS model with the open-boundary condition is outlined.

The impact of neighboring infection on the computer virus spread in packets on scale-free networks

In this paper, we introduce the effect of neighbors on the infection of packets by computer virus in the SI and SIR models using the minimal traffic routing protocol. We have applied this model to the Barabasi–Albert network to determine how intrasite and extrasite infection rates affect virus propagation through the traffic flow of information packets in both the free-flow and the congested phases. The numerical results show that when we change the intrasite infection rate [Formula: see text] while keeping constant the extrasite infection rate [Formula: see text], we get normal behavior in the congested phase: in the network, the proportion of infected packets increases to reach a peak and then decreases resulting in a simultaneous increase of the recovered packets. In contrast, when the intrasite infection rate [Formula: see text] is kept fixed, an increase of the extrasite infection rate results in two regimes: The first one is characterized by an increase of the proportion of infected packets until reaching some peak value and then decreases smoothly. The second regime is characterized by an increase of infected packets to some stationary value.

Empirical exploration of air traffic and human dynamics in terminal airspaces

We propose a multi-layer network approach to model and analyze air traffic terminal networks, which are viewed as complex, task-critical, techno-social systems with numerous interactions among airspaces, procedures, aircraft, and air traffic controllers (ATCOs). Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) are developed to represent critical physical and operational characteristics. Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from ATCOs, respectively. Furthermore, a set of analytical metrics, including network variables, complex network attributes, controllers’ cognitive complexity, and chaos metrics, are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of “ATCOs-flow” interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in the conflict system and human behavioral system when traffic switches to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.

Analyzing Microscopic Behavioral between Two Phases of Follower and Leader in Traffic Oscillation with Developing Artificial Neural Networks

A Sudden speed drop in the leader vehicle of vehicle platoon results in propagating the deceleration wave from downstream towards the upstream flow. Points of wave propagation of the leader vehicle towards the follower vehicle identification are done based on Newell’s theory in trajectory data. Deceleration wave propagates based on two parameters, time and space, τ- δ. A follower driver performs different behavioural reactions that they result in deviating follower driver from Newell’s trajectory. In this paper, follower driver behaviour was identified based on two theories. The asymmetric microscopic driving behaviour theory and traffic hysteresis were used during the deceleration and acceleration phases, respectively. The data trajectories were classified into different traffic phases. Driver’s parameters were identified at the microscopic level. Since the follower driver had the nonlinear behaviour, artificial neural networks were developed. They were able to analysis and identify effective parameters of dependent variable between deceleration phases leading to congestion phase, based on the behavioural patterns. Analysis results present effective parameters based on any behavioural patterns. Spacing difference of two phases, deceleration and congestion phases, was the most effective parameter of both two behavioural patterns, under reaction – timid and over reaction – timid. Increasing the spacing difference of two phases results in decreasing (increasing) time based on under reaction – timid (over reaction – timid)..

General phase transition models for vehicular traffic with point constraints on the flow

In this paper, we present a general phase transition model that describes the evolution of vehicular traffic along a one‐lane road. Two different phases are taken into account, according to whether the traffic is low or heavy. The model is given by a scalar conservation law in the free‐flow phase and by a system of 2 conservation laws in the congested phase. The free‐flow phase is described by a one‐dimensional fundamental diagram corresponding to a Newell‐Daganzo type flux. The congestion phase is described by a two‐dimensional fundamental diagram obtained by perturbing a general fundamental flux. In particular, we study the resulting Riemann problems in the case a local point constraint on the flow of the solutions is enforced.

Optimal topology to minimizing congestion in connected communication complex network

In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that [Formula: see text] the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.

A macroscopic traffic model with phase transitions and local point constraints on the flow

In this paper we present a macroscopic phase transition model with a local point constraint on the flow. Its motivation is, for instance, the modelling of the evolution of vehicular traffic along a road with pointlike inhomogeneities characterized by limited capacity, such as speed bumps, traffic lights, construction sites, toll booths, etc. The model accounts for two different phases, according to whether the traffic is low or heavy. Away from the inhomogeneities of the road the traffic is described by a first order model in the free-flow phase and by a second order model in the congested phase. To model the effects of the inhomogeneities we propose two Riemann solvers satisfying the point constraints on the flow.

Predicting traffic congestion using driver behavior

Traffic congestion is one of the largest traffic problems and one solution is a prediction of its generation. Traffic congestion comes from three phases order: the free travel phase, the meta-stability phase, and the traffic congestion phase. Therefore, it can be considered that if the meta-stability phase can be detected, forecasting traffic congestion becomes possible. This paper proposes a driver model that forecasts traffic congestion based on changes in driving behavior and that does not rely on traffic flow monitoring infrastructure. As a result of evaluation in driving simulators, it was understood that the distribution of steering, throttle and speed input frequency changes based on changes in the travel phase. It is possible to distinguish these changes using support vector machines as shown in Figure 1, and it is possible to make this into a driver model that predicts traffic congestion. This method is the first propose that uses only CAN (Controller Area Network) data and needs no additional sensors to detect driving environments or any infrastructures.

Transition to congestion in communication/computation networks for near-optimal and sub-optimal resource management via Monte Carlo simulations

We generalize previous studies on critical phenomena in communication networks by adding computational capabilities to the nodes to better describe real-world situations such as cloud computing. A set of tasks with random origins and destinations characterized by a multi-tier computational structure is distributed on a network modeled as a graph. The distribution of execution times (or latencies) of all tasks is statically computed for several initial workloads and its sum is used as the target function to be optimized by Simulated Annealing. A canonical Monte Carlo simulation allows to analyze the network behavior at different levels of sub-optimality of resource management (achieved by changing the temperature). We first study the static transition to congestion of the whole network by varying temperature and workload. Then, we propose a method to approximately recover the evolution of the system in time, by interpolating the static latency probability distributions. This allows to study the dynamic transition to the congested phase by introducing a variable task-production rate, as usually studied in the literature, without the need of explicit time-consuming simulations. We are able to reproduce the main published results on network congestion and to gain a deeper insight over the maximum theoretical performance of a system and its sensitivity to sub-optimality of routing and load balancing.

Fatigue effect on phase transition of pedestrian movement: experiment and simulation study

How to model pedestrian movement is an intriguing problem in the area of statistical physics. As a common phenomenon of pedestrian movement, fatigue has a significant negative effect on pedestrian movement, especially when pedestrians move or run with heavy luggage, rescue the wounded in disaster, climb stairs and etc. According to the field observations and previous researches, fatigue coefficient is defined as the decrease of desired velocity in this study. However, previous researches lacked quantitative analysis of the effect of fatigue on pedestrian speed. It has been a great challenge to study the effect of fatigue on pedestrian flow, since pedestrians of heterogeneous walking abilities and the change of pedestrians’ moving properties need to be taken into consideration. Thus, at first, a series of pedestrian experiments, under three different conditions, were conducted to formulate the empirical relationship among fatigue, average free velocity, and walking distance. Then the empirical formulation of pedestrian fatigue was imported into the multi-velocity field floor cellular automata (FFCA) model for following pedestrian dynamics analysis. The velocity ratio was adjusted dynamically to adapt the change of pedestrians’ velocity due to fatigue. The fatigue, entrance flow rate and pedestrian’s initial desired velocity are found to have significant effects on the pedestrian flow. The space-time distributions of pedestrian density and velocity were explored in detail, with phase transition analyses from a free flow phase to a congestion phase. Additionally, the ‘density wave’ in the system can be observed if a certain ratio of burdened pedestrians lay in the high density region. The envelope of the ‘density wave’ reaches its maximum amplitude around the entrance position, and gradually diminishes away from the entrance.

Simple stochastic cellular automaton model for starved beds and implications about formation of sand topographic features in terms of sand flux

A simple stochastic cellular automaton model is proposed for simulating bedload transport, especially for cases with a low transport rate and where available sediments are very sparse on substrates in a subaqueous system. Numerical simulations show that the bed type changes from sheet flow through sand patches to ripples as the amount of sand increases; this is consistent with observations in flume experiments and in the field. Without changes in external conditions, the sand flux calculated for a given amount of sand decreases over time as bedforms develop from a flat bed. This appears to be inconsistent with the general understanding that sand flux remains unchanged under the constant-fluid condition, but it is consistent with the previous experimental data. For areas of low sand abundance, the sand flux versus sand amount (flux–density relation) in the simulation shows a single peak with an abrupt decrease, followed by a long tail; this is very similar to the flux–density relation seen in automobile traffic flow. This pattern (the relation between segments of the curve and the corresponding bed states) suggests that sand sheets, sand patches, and sand ripples correspond respectively to the free-flow phase, congested phase, and jam phase of traffic flows. This implies that sand topographic features on starved beds are determined by the degree of interference between sand particles. Although the present study deals with simple cases only, this can provide a simplified but effective modeling of the more complicated sediment transport processes controlled by interference due to contact between grains, such as the pulsatory migration of grain-size bimodal mixtures with repetition of clustering and scattering.