Large scale traffic simulations

Nagel, Kai; Rickert, M.; Barrett, Christopher L.

doi:10.2172/459884

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…Using ideas from the theory of percolation phase transitions and the [12] cellular automata model (NaSch model) for numerical experiments, the critical exponent of jam durations was found to be very close to τ = 3/2 and suggested the relationship with the first return time of a one-dimensional random walk. Shortly after, the same authors [13] presented numerical evidence of SOC on a simple network and argued that conventional traffic control would tend to enlarge the area of the network under criticality. They suggested that an effective management strategy would be to force the system away from criticality through less efficient traffic management.…”

Section: Evidence Of Network Criticalitymentioning

confidence: 99%

“…Surprisingly, the impact of SOC in the field of traffic flow theory has been underwhelming [15,16]. Apart from [13,17,18] who present some numerical evidence of SOC on networks, it appears that the subject has gone largely under the radar. One reason might be that the main insight from the early works [6,10] was that SOC in the NaSch model emerges only in "cruise-control" mode, where random perturbations are only allowed in congested traffic states.…”

Section: Evidence Of Network Criticalitymentioning

confidence: 99%

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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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Section: Evidence Of Network Criticalitymentioning

confidence: 99%