Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory

Lyu, Zelin; Hu, Xiaojian; Zhang, Fang; Liu, Tenghui; Cui, Zhiwei

doi:10.1016/j.physa.2021.126471

Cited by 9 publications

(1 citation statement)

References 56 publications

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“…Trucks, in particular, have lower handling stability than passenger cars, resulting in higher casualty rates in traffic accidents (Li et al, 2016). Due to their greater weight, trucks generally have slower speeds than passenger cars, especially in specific road sections, where speeds can drop to 30 km/h or even lower (Lyu et al, 2022). These slow-moving trucks create mobile bottlenecks on the road (Krampe and Junge, 2021), significantly reducing the running speeds of multiple road sections and degrading the overall road service level (Gashaw et al, 2018;Jamshidnejad et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Wang,

Dong,

Shao

et al. 2024

Front. Eng. Manag.

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Accurate traffic state estimations (TSEs) within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies. Traditional TSE methods often assume homogeneous traffic, where all vehicles are considered identical, which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics. In this study, we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows. We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements: basic traffic parameters, heterogeneous vehicle speeds, heterogeneous vehicle flows, and mixed flow rates. This system aids in capturing the unique traffic characteristics of different vehicle types. Our proposed high-dimensional fuzzy TSE model, termed HiF-TSE, integrates three main processes: feature selection, which eliminates redundant traffic features using Spearman correlation coefficients; dimension reduction, which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data; and FCM clustering, which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters. The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing. We validate our model through a real-world case study, demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.

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