Road section traffic risk propagation model based on macro traffic flow energy consumption

Traffic risk is an important source of road traffic accidents. Effectively predicting the propagation of accident risks on the road after an accident is of great significance for preventing secondary accidents. Therefore, this paper establishes a road section accident risk propagation model based on macro traffic flow energy consumption. Firstly, the influence of lane occupation and vehicle lane changing after the accident on traffic flow is analyzed, and a macro traffic flow model under the risk after the accident is constructed. Secondly, combined with the definition of traffic flow energy dissipation, the movement of the whole vehicle is abstracted as a fluid movement process, and a traffic flow energy consumption model under the risk after the accident is proposed to quantify the accident risk. Finally, according to the spatiotemporal correlation of risk propagation, a risk impact range and duration calculation model is proposed to describe the risk propagation process. In order to verify the effectiveness of the risk propagation model proposed in this paper, MATLAB and VISSIM are used to carry out numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities. The experimental results show that the greater the initial density of the road section when the accident occurs, the wider the risk impact range and the longer the duration. The absolute value of the prediction deviation rate of the proposed model is below 8%, which has a good prediction effect.

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Publication Details

Journal
Scientific insights and discoveries review
Published
2024-10-11
DOI
https://doi.org/10.59782/sidr.v3i1.128
Primary Topic
Traffic control and management
Type
article
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article

Road section traffic risk propagation model based on macro traffic flow energy consumption

Caixia Han, Ting Xu, Xianmin Song, Xin Liu et al.
Scientific insights and discoveries review
Traffic control and management
article

Road section traffic risk propagation model based on macro traffic flow energy consumption

Caixia Han, Ting Xu, Xianmin Song, Xin Liu, Shan Jiang, Fang Wenbo
article en

Abstract

Traffic risk is an important source of road traffic accidents. Effectively predicting the propagation of accident risks on the road after an accident is of great significance for preventing secondary accidents. Therefore, this paper establishes a road section accident risk propagation model based on macro traffic flow energy consumption. Firstly, the influence of lane occupation and vehicle lane changing after the accident on traffic flow is analyzed, and a macro traffic flow model under the risk after the accident is constructed. Secondly, combined with the definition of traffic flow energy dissipation, the movement of the whole vehicle is abstracted as a fluid movement process, and a traffic flow energy consumption model under the risk after the accident is proposed to quantify the accident risk. Finally, according to the spatiotemporal correlation of risk propagation, a risk impact range and duration calculation model is proposed to describe the risk propagation process. In order to verify the effectiveness of the risk propagation model proposed in this paper, MATLAB and VISSIM are used to carry out numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities. The experimental results show that the greater the initial density of the road section when the accident occurs, the wider the risk impact range and the longer the duration. The absolute value of the prediction deviation rate of the proposed model is below 8%, which has a good prediction effect.

Scientific insights and discoveries reviewVol. 3
Affordable and clean energy
Openalex Percentile: Top 80%
Traffic control and management
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Road section traffic risk propagation model based on macro traffic flow energy consumption — Caixia Han, Ting Xu, et al. · Scientific insights and discoveries review (2024) | TGRS Research Map | TGRS