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. A direct consequence is that conventional traffic management strategies seeking to maximize the flow may be detrimental as they make the system more unpredictable and more prone to collapse. Other implications for traffic flow in the capacity state are discussed, such as: \item jam sizes obey a power-law distribution with exponents 1/2, implying that both its mean and variance diverge to infinity, and therefore traditional statistical methods fail for prediction and control, \item the tendency to be at the critical state is an intrinsic property of traffic flow driven by our desire to travel at the maximum possible speed, \item traffic flow in the critical region is chaotic in that it is highly sensitive to initial conditions, \item aggregate 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. This fractal nature of traffic flow calls for analysis methods currently not used in our field.
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.
Commenter: Jorge Laval
Commenter's Conflict of Interests: Author