Version 1
: Received: 3 August 2021 / Approved: 5 August 2021 / Online: 5 August 2021 (08:02:42 CEST)
Version 2
: Received: 8 September 2021 / Approved: 9 September 2021 / Online: 9 September 2021 (15:58:11 CEST)
Version 3
: Received: 2 February 2023 / Approved: 3 February 2023 / Online: 3 February 2023 (13:50:11 CET)
Laval, J. A. Self-Organized Criticality of Traffic Flow: Implications for Congestion Management Technologies. Transportation Research Part C: Emerging Technologies, 2023, 149, 104056. https://doi.org/10.1016/j.trc.2023.104056.
Laval, J. A. Self-Organized Criticality of Traffic Flow: Implications for Congestion Management Technologies. Transportation Research Part C: Emerging Technologies, 2023, 149, 104056. https://doi.org/10.1016/j.trc.2023.104056.
Laval, J. A. Self-Organized Criticality of Traffic Flow: Implications for Congestion Management Technologies. Transportation Research Part C: Emerging Technologies, 2023, 149, 104056. https://doi.org/10.1016/j.trc.2023.104056.
Laval, J. A. Self-Organized Criticality of Traffic Flow: Implications for Congestion Management Technologies. Transportation Research Part C: Emerging Technologies, 2023, 149, 104056. https://doi.org/10.1016/j.trc.2023.104056.
Abstract
This paper shows that the kinematic wave model exhibits self-organized criticality when initialized with random initial conditions around the critical density. This has several important implications for traffic flow in the capacity state, such as: \item jam sizes obey a power law distribution with exponents 1/2, implying that both the mean and variance diverge to infinity, \item self-organization is an intrinsic property of traffic flow models in general, independently of other random perturbations, \item this critical behavior is a consequence of the flow maximization objective of traffic flow models, which can be observed on a density range around the critical density that depends on the length of the segment, \item typical measures of performance are proportional to the area under a Brownian excursion, and therefore are given by different scalings of the Airy distribution, \item traffic in the time-space diagram forms self-affine fractals where the basic unit is a triangle, in the shape of the fundamental diagram, containing 3 traffic states: voids, capacity and jams.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
