Effect of the trip-length distribution on network-level traffic dynamics: Exact and statistical results

Laval, Jorge A.

doi:10.1016/j.trc.2023.104036

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…The impacts of the traffic flow model used should also be investigated. It has been argued [4,28] that criticality in traffic flow can only be described realistically using a microscopic framework, even in the case of MFD inspired modeling [29]. In fact, the lack of awareness regarding the critical behavior in traffic flow prevalent today in our community [21] can be explained by our use of aggregated models such as the cell transmission model or any PDE-inspired numerical solution method.…”

Section: Discussionmentioning

confidence: 99%

Urban Network Percolation and Traffic Dynamics: Causality Relationships in Numerical Experiments

Laval¹,

Menéndez²

2023

Preprint

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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.

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