Mitigation of self-organized traffic jams using cooperative adaptive cruise control

Kim, Taehooie; Jerath, Kshitij

doi:10.1109/iccve.2016.2

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…For example, studies have examined the effect of CVs on traffic flow in diverse scenarios including signalized intersections [32], bottlenecks [8], highway congestion [33], and urban road networks [34]. Similarly, other research works have studied the impacts on traffic flow arising from distinct implementation schemes including experimental platoons [35], cooperative highway traffic [36], and in mixed traffic flows [37,38], among others. In the current context, another interesting aspect that influences traffic flow is communication of information to drivers via on-board units which can cause associated changes to driving behaviors such as distracted driving [39,40].…”

Section: Influencing Traffic Flow With Connected Vehiclesmentioning

confidence: 99%

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

Jerath,

Gayah,

Brennan

2024

PLoS ONE

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We present a novel perspective on how connected vehicles can reduce total vehicular delay arising due to the capacity drop phenomenon observed at fixed freeway bottlenecks. We analytically determine spatial regions upstream of the bottleneck, called zones of influence, where a pair of connected vehicles can use an event-triggered control policy to positively influence a measurable traffic macrostate, e.g., the total vehicular delay at bottlenecks. These analytical expressions are also able to determine the boundaries (called null and event horizons) of these spatial extents, outside of which a connected vehicle cannot positively influence the traffic macrostate. These concepts can help ensure that information is disseminated to connected vehicles in only those spatial regions where it can be used to positively impact traffic macrostates. Some scenarios examined in this study indicate that communication between connected vehicles may be required over a span of several kilometers to positively impact traffic flow and mitigate delays arising due to the capacity drop phenomenon.

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Section: Influencing Traffic Flow With Connected Vehiclesmentioning

confidence: 99%

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

Jerath,

Gayah,

Brennan

2024

PLoS ONE

Self Cite

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“…In contrast to microscopic-and macroscopic-scale modeling approaches, mesoscopic-scale models have been proposed as a means to capture dynamics associated with intermediate scales [14,15,16,17]. The mesoscopic modeling approach was first proposed by Ilya Prigogine using evolution of speed and position probability distributions [18].…”

Section: Introductionmentioning

confidence: 99%

Renormalization Group Approach to Cellular Automata-Based Multi-scale Modeling of Traffic Flow

Yang

Haeri

Jerath

2021

Unifying Themes in Complex Systems X

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Traffic flow modeling is typically performed at one of three different scales (microscopic, mesoscopic, or macroscopic), each with distinct modeling approaches. Recent works that attempt to merge models at different scales have yielded some success, but there still exists a need for a single modeling framework that can seamlessly model traffic flow across several spatiotemporal scales. The presented work utilizes a renormalization group (RG) theoretic approach, building upon our prior research on statistical mechanics-inspired traffic flow modeling. Specifically, we use an Ising model-inspired cellular automata model to represent traffic flow dynamics. RG transformations are applied to this model to obtain coarse-grained parameters (interaction and field coefficients) to simulate traffic at coarser spatiotemporal scales and different vehicular densities. We measure the accuracy of the coarse-grained traffic flow simulation using a pixel-based image correlation metric and find good correlation between the dynamics at different scales. Importantly, emergent traffic dynamics such as backward moving congestion waves are retained at coarser scales with this approach. The presented work has the potential to spur the development of a unified traffic flow modeling framework for transportation analysis across varied spatiotemporal scales, while retaining an analytical relationship between the model parameters at these scales.

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Emerging Freeway Traffic Control Strategies

Ferrara

Sacone

Siri

2018

Freeway Traffic Modelling and Control

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Mitigation of self-organized traffic jams using cooperative adaptive cruise control

Cited by 4 publications

References 14 publications

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

Renormalization Group Approach to Cellular Automata-Based Multi-scale Modeling of Traffic Flow

Emerging Freeway Traffic Control Strategies

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