TY - JOUR
T1 - Percolation properties in a traffic model
AU - Wang, Feilong
AU - Li, Daqing
AU - Xu, Xiaoyun
AU - Wu, Ruoqian
AU - Havlin, Shlomo
N1 - Publisher Copyright:
© 2015 EPLA.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - As a dynamical complex system, traffic is characterized by a transition from free flow to congestions, which is mostly studied in highways. However, despite its importance in developing congestion mitigation strategies, the understanding of this common traffic phenomenon in a city scale is still missing. An open question is how the traffic in the network collapses from a global efficient traffic to isolated local flows in small clusters, i.e. the question of traffic percolation. Here we study the traffic percolation properties on a lattice by simulation of an agent-based model for traffic. A critical traffic volume in this model distinguishes the free state from the congested state of traffic. Our results show that the threshold of traffic percolation decreases with increasing traffic volume and reaches a minimum value at the critical traffic volume. We show that this minimal threshold is the result of longest spatial correlation between traffic flows at the critical traffic volume. These findings may help to develop congestion mitigation strategies in a network view.
AB - As a dynamical complex system, traffic is characterized by a transition from free flow to congestions, which is mostly studied in highways. However, despite its importance in developing congestion mitigation strategies, the understanding of this common traffic phenomenon in a city scale is still missing. An open question is how the traffic in the network collapses from a global efficient traffic to isolated local flows in small clusters, i.e. the question of traffic percolation. Here we study the traffic percolation properties on a lattice by simulation of an agent-based model for traffic. A critical traffic volume in this model distinguishes the free state from the congested state of traffic. Our results show that the threshold of traffic percolation decreases with increasing traffic volume and reaches a minimum value at the critical traffic volume. We show that this minimal threshold is the result of longest spatial correlation between traffic flows at the critical traffic volume. These findings may help to develop congestion mitigation strategies in a network view.
UR - http://www.scopus.com/inward/record.url?scp=84948471716&partnerID=8YFLogxK
U2 - 10.1209/0295-5075/112/38001
DO - 10.1209/0295-5075/112/38001
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AN - SCOPUS:84948471716
SN - 0295-5075
VL - 112
JO - EPL
JF - EPL
IS - 3
M1 - 38001
ER -