Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhase transition and hysteresis in scale-free network traffic
50
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We model information traffic on scale-free networks by introducing the node queue length L proportional to the node degree and its delivering ability C proportional to L. The simulation gives the overall capacity of the traffic system, which is quantified by a phase transition from free flow to congestion. It is found that the maximal capacity of the system results from the case of the local routing coefficient phi slightly larger than zero, and we provide an analysis for the optimal value of phi. In addition, we report for the first time the fundamental diagram of flow against density, in which hysteresis is found, and thus we can classify the traffic flow with four states: free flow, saturated flow, bistable, and jammed.
rate research
Read More
We model information traffic on scale-free networks by introducing the bandwidth as the delivering ability of links. We focus on the effects of bandwidth on the packet delivering ability of the traffic system to better understand traffic dynamic in real network systems. Such ability can be measured by a phase transition from free flow to congestion. Two cases of node capacity C are considered, i.e., C=constant and C is proportional to the nodes degree. We figured out the decrease of the handling ability of the system together with the movement of the optimal local routing coefficient $alpha_c$, induced by the restriction of bandwidth. Interestingly, for low bandwidth, the same optimal value of $alpha_c$ emerges for both cases of node capacity. We investigate the number of packets of each node in the free flow state and provide analytical explanations for the optimal value of $alpha_c$. Average packets traveling time is also studied. Our study may be useful for evaluating the overall efficiency of networked traffic systems, and for allevating traffic jam in such systems.
This letter propose a new model for characterizing traffic dynamics in scale-free networks. With a replotted road map of cities with roads mapped to vertices and intersections to edges, and introducing the road capacity L and its handling ability at intersections C, the model can be applied to urban traffic system. Simulations give the overall capacity of the traffic system which is quantified by a phase transition from free flow to congestion. Moreover, we report the fundamental diagram of flow against density, in which hysteresis is found, indicating that the system is bistable in a certain range of vehicle density. In addition, the fundamental diagram is significantly different from single-lane traffic model and 2-D BML model with four states: free flow, saturated flow, bistable and jammed.
We perform statistical analysis of the single-vehicle data measured on the Dutch freeway A9 and discussed in Ref. [2]. Using tools originating from the Random Matrix Theory we show that the significant changes in the statistics of the traffic data can be explained applying equilibrium statistical physics of interacting particles.
Self-similarity in the network traffic has been studied from several aspects: both at the user side and at the network side there are many sources of the long range dependence. Recently some dynamical origins are also identified: the TCP adaptive congestion avoidance algorithm itself can produce chaotic and long range dependent throughput behavior, if the loss rate is very high. In this paper we show that there is a close connection between the static and dynamic origins of self-similarity: parallel TCPs can generate the self-similarity themselves, they can introduce heavily fluctuations into the background traffic and produce high effective loss rate causing a long range dependent TCP flow, however, the dropped packet ratio is low.
To provide a more accurate description of the driving behaviors in vehicle queues, a namely Markov-Gap cellular automata model is proposed in this paper. It views the variation of the gap between two consequent vehicles as a Markov process whose stationary distribution corresponds to the observed distribution of practical gaps. The multiformity of this Markov process provides the model enough flexibility to describe various driving behaviors. Two examples are given to show how to specialize it for different scenarios: usually mentioned flows on freeways and start-up flows at signalized intersections. The agreement between the empirical observations and the simulation results suggests the soundness of this new approach.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
