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Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirection...

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Autores principales: Yi, Ziwei, Lu, Wenqi, Qu, Xu, Li, Linheng, Mao, Peipei, Ran, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706987/
https://www.ncbi.nlm.nih.gov/pubmed/34960416
http://dx.doi.org/10.3390/s21248322
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author Yi, Ziwei
Lu, Wenqi
Qu, Xu
Li, Linheng
Mao, Peipei
Ran, Bin
author_facet Yi, Ziwei
Lu, Wenqi
Qu, Xu
Li, Linheng
Mao, Peipei
Ran, Bin
author_sort Yi, Ziwei
collection PubMed
description Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β(1) or β(2) is the biggest the best for the traffic.
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spelling pubmed-87069872021-12-25 Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis Yi, Ziwei Lu, Wenqi Qu, Xu Li, Linheng Mao, Peipei Ran, Bin Sensors (Basel) Article Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β(1) or β(2) is the biggest the best for the traffic. MDPI 2021-12-13 /pmc/articles/PMC8706987/ /pubmed/34960416 http://dx.doi.org/10.3390/s21248322 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yi, Ziwei
Lu, Wenqi
Qu, Xu
Li, Linheng
Mao, Peipei
Ran, Bin
Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title_full Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title_fullStr Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title_full_unstemmed Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title_short Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis
title_sort controlling the connected vehicle with bi-directional information: improved car-following models and stability analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706987/
https://www.ncbi.nlm.nih.gov/pubmed/34960416
http://dx.doi.org/10.3390/s21248322
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