Version 1
: Received: 18 April 2023 / Approved: 20 April 2023 / Online: 20 April 2023 (05:07:26 CEST)
How to cite:
Laval, J.; Menendez, M. Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments. Preprints2023, 2023040548. https://doi.org/10.20944/preprints202304.0548.v1
Laval, J.; Menendez, M. Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments. Preprints 2023, 2023040548. https://doi.org/10.20944/preprints202304.0548.v1
Laval, J.; Menendez, M. Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments. Preprints2023, 2023040548. https://doi.org/10.20944/preprints202304.0548.v1
APA Style
Laval, J., & Menendez, M. (2023). Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments. Preprints. https://doi.org/10.20944/preprints202304.0548.v1
Chicago/Turabian Style
Laval, J. and Monica Menendez. 2023 "Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments" Preprints. https://doi.org/10.20944/preprints202304.0548.v1
Abstract
It was recently shown that urban networks might exhibit two different critical points, one for the percolation of congested links through the network, and another one for the flow of vehicles through the network. It was observed that the percolation critical point happens after the flow critical point, where network throughput is maximized. This result is important because if a causal relationship exists between these two processes, it can be leveraged for improving control methods to avoid network collapse. This paper presents the results of numerical experiments to understand the possible causal relationship between the percolation and flow processes by focusing on the relative timing between the two critical points. The goal is to understand the impacts of important factors such as: (i) urban network type: long- and short-block networks, (ii) signal timing control type: fixed, random, responsive, and (iii) route choice: turning proportions at intersections. We find that the threshold density $k^*_c$ to determine if a link is congested has the main impact on the timing of the percolation transition, where lower values trigger earlier transitions. This implies that $k^*_c$ can be set such that the percolation transition happens before the flow critical point, opening the door for improved congestion management strategies. If $k^*_c$ is set to the average network critical density, maximum network throughput happen simultaneously with the percolation transition. Surprisingly, it appears that these results, and more generally, the relationship between the percolation and flow processes is independent of (i) and (ii) above.
Engineering, Transportation Science and Technology
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.
