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
Self-organized criticality (SOC) is a celebrated paradigm from the 90’s for understanding dynamical systems naturally driven to its critical point, where the power-law dynamics taking place make predictions practically impossible, such as in stock prices, earthquakes, pandemics and many other problems in science related to phase transitions. Shortly thereafter, it was realized that traffic flow might be in the SOC category, implying that conventional traffic management strategies seeking to maximize the local flows can become detrimental. This paper shows that the Kinematic Wave model with triangular fundamental diagram, and many other related traffic models, indeed exhibit SOC, thanks in partto the fractal nature of traffic exposed here on the one hand, and our need to get to our destinations as soon as possible, on the other hand. Important implications for congestion management of traffic near the critical region are discussed, such as:(i) Jam sizes obey a power-law distribution with exponent 1/2, implying that both its mean and variance become ill-defined and therefore impossible to estimate. (ii) Traffic in the critical region is chaotic in the sense that predictions becomes extremely sensitive to initial conditions. (iii) However, aggregate measures of performance such as delays and average speeds are not heavy tailed, and can be characterized exactly by different scalings of the Airy distribution, (iv) Traffic state time-space “heat maps” are 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 trafficflow 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