Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

Wenlong Liu, Yixin Chen, Hongtao Li and Hui Zhang
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Wenlong Liu: Transportation College, Jilin University, Changchun 130022, China
Yixin Chen: Transportation College, Jilin University, Changchun 130022, China
Hongtao Li: Transportation College, Jilin University, Changchun 130022, China
Hui Zhang: China FAW Group Corporation Co., Ltd., No. 1, Honaqi Street, Changchun 130013, China

Sustainability, 2022, vol. 14, issue 10, 1-21

Abstract: With the development of the drive of electronic communication technology, the driving assistance system that perceives the external traffic environment has developed rapidly. However, when quantifying the complexity of the road traffic environment without fully considering the driving characteristics and subjective feelings, the false alarm rate of the driving warning system increases and affects the early warning effect. In order to more accurately quantify the complexity of the road traffic environment, we analyzed the impact of road traffic environment changes on drivers under the condition of car-following. Firstly, we selected the influencing factors of the traffic environment complexity, such as the driving operation indicators, the vehicle driving status indicators and the road environmental indicators. The weight calculation model of each influence factor is established based on the principal component analysis method. Secondly, the driver’s reaction time during car-following is used as the quantitative index of road traffic environment complexity. The quantitative model of road traffic environment complexity is constructed combined with the weight of road traffic environment complexity. Finally, the driving simulation experiment is designed to verify the complexity quantification model of the road traffic environment. The road traffic environment complexity value calculated in our study is better than the TTC, and the early-warning threshold is raised by 2–5%. The research conclusion can provide a basis for the design of the car alarm system.

Keywords: traffic safety; road traffic environment complexity; car-following model; principal component analysis; vehicle warning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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