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Erschienen in: International Journal of Automation and Computing 1/2014

01.02.2014

A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Traffic Dynamics

verfasst von: Georges M. Arnaout, Shannon Bowling

Erschienen in: Machine Intelligence Research | Ausgabe 1/2014

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Abstract

Cooperative adaptive cruise control (CACC) vehicles are intelligent vehicles that use vehicular ad hoc networks (VANETs) to share traffic information in real time. Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration. Since reaching a high CACC market penetration level is not occurring in the near future, this study presents a progressive deployment approach that demonstrates to have a great potential of reducing traffic congestions at low CACC penetration levels. Using a previously developed microscopic traffic simulation model of a freeway with an on-ramp — created to induce perturbations and trigger stop-and-go traffic, the CACC system’s effect on the traffic performance is studied. The results show significance and indicate the potential of CACC systems to improve traffic characteristics which can be used to reduce traffic congestion. The study shows that the impact of CACC is positive and not only limited to a high market penetration. By giving CACC vehicles priority access to high-occupancy vehicle (HOV) lanes, the highway capacity could be significantly improved with a CACC penetration as low as 20%.

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Fußnoten
1
Note that although the traffic flow formula used applies only to the space mean speed (and not the time mean speed we are using in this study), the time mean speed is always higher than the space mean speed. Therefore the space mean speed will follow the same decreasing behavior as the time mean speed validating the assumption made.
 
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Metadaten
Titel
A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Traffic Dynamics
verfasst von
Georges M. Arnaout
Shannon Bowling
Publikationsdatum
01.02.2014
Verlag
Springer-Verlag
Erschienen in
Machine Intelligence Research / Ausgabe 1/2014
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI
https://doi.org/10.1007/s11633-014-0760-2

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