Paper
8 May 2022 Modelling acceleration lane length in CAV lane merge area based on breakdown probability
Xinjian Lv, Rui Gan, Luhai Liu, Haozhan Ma, Peipei Mao, Xu Qu
Author Affiliations +
Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022); 122492A (2022) https://doi.org/10.1117/12.2636777
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 2022, Xiamen, China
Abstract
The emergence of connected and automated vehicles (CAVs) shed light on future traffic development. In near future, due to the mix of human driven vehicles (HDVs)and CAVs, the two types of vehicles can be separated by setting up dedicated CAV lanes for autonomous driving, thus ensuring the operational efficiency, traffic safety and the environment-friendly property of CAVs. In freeways with CAV lanes, the merge area formed by the convergence of ramps is the key to ensuring the high speed and stability of CAVs. The design of the acceleration lane length in the merge area is the core of the operation of the entire merge area, however, the traditional method has certain shortcomings due to its reliance on the gap acceptance theory. Recent literature has shown that gap acceptance theory cannot account for practical traffic phenomenon such as active avoidance and forced merging. Firstly, this paper introduces the statistical method of breakdown events and proposes a breakdown event probability model for highway merge areas considering CAV lanes. The relationship between breakdown event probability and the length of the acceleration lane in the merge area is established through the derivation of equations. Then, based on the above BP model, a method to calculate the length of acceleration lanes in merge area is proposed. Considering 13 flow combinations, the variation of probability of breakdown versus acceleration lane length is plotted. Employing the method proposed in this paper, the length of the acceleration lane can be determined when the flow combination and BP are given. The length of the acceleration lane can be determined when the method proposed in this paper is employed, given flow combination and BP. The results of this paper provide a solid theoretical basis for the future CAV lane setting.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xinjian Lv, Rui Gan, Luhai Liu, Haozhan Ma, Peipei Mao, and Xu Qu "Modelling acceleration lane length in CAV lane merge area based on breakdown probability", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 122492A (8 May 2022); https://doi.org/10.1117/12.2636777
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KEYWORDS
Data modeling

Probability theory

Roads

Safety

Analytical research

Modeling

Statistical modeling

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