Multiple factors on driving load in mountain area at night based on factor analysis

Li, Hao; Jiang, Heng; Yang, Jiabao · 2024 · Crossref

DOI: 10.1371/journal.pone.0315180

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Summary

This study investigates the impact of multiple environmental and operational factors on driver workload during nighttime driving in mountainous areas, a context associated with high accident rates and limited prior research. Motivated by the need to reduce traffic accidents by understanding how adverse conditions affect driver psychology and physiology, the authors examined three primary categories of influence: external weather environments, road driving environments, and in-vehicle driving environments. The research specifically aimed to quantify visual load under combined adverse factors to identify safer driving configurations. The methodology employed a simulated driving experiment using UC-win/Road software to model a 10 km section of Provincial Highway S208 in Chongyang County, China, characterized by a 4% gradient and two-lane configuration. Twenty licensed drivers (14 male, 6 female, mean age 30.1 years) participated in the study. Visual behavior was monitored using Dikablis Glasses 3 eye-tracking devices, capturing four key indicators: rate of change in pupil area ($R_t$), fixation duration ($T_d$), saccade velocity ($V_S$), and dynamic change of saccade angle ($\Delta S_a$). The experimental design manipulated four factors: plant spacing (3m, 6m, 9m), types of traffic auxiliary facilities (0, 1, or 2 types), traffic volume (0, 1, or 2 vehicles per minute), and driving sub-missions (none, radio listening, or hands-free cognitive task). Data were processed using the Lyddane-Shindo method and analyzed via factor analysis to establish a quantitative model of driving load. The results indicated that specific environmental configurations significantly reduced driver distraction and workload. Driver distraction was minimized when plant spacing was set at 6 meters and two types of road traffic auxiliary facilities were present. The lowest overall driving workload occurred when traffic flow was zero and no driving sub-missions were performed. Analysis of individual factors revealed that narrow plant spacing (3m) increased stress due to restricted views, while the absence of traffic facilities increased psychological stress due to poor road linearity anticipation. Higher traffic volumes and cognitive sub-missions, particularly hands-free calls requiring calculation, increased workload by occupying auditory and cognitive resources. Factor analysis confirmed that $R_t$, $V_S$, and $T_d$ positively correlated with driving load, while $\Delta S_a$ negatively correlated, allowing for the construction of a comprehensive load index. The study concludes that optimizing roadside vegetation spacing and ensuring clear, compliant traffic signage are critical for reducing nighttime driving load in mountainous regions. The findings provide theoretical support for traffic safety theories and offer practical guidelines for road design and management to mitigate risks associated with complex driving environments. By quantifying the combined effects of multiple factors, the research contributes to a deeper understanding of driver visual perception and workload, aiding in the development of safer driving environments.

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Topics

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• mental demand
• road complexity
• traffic density
• dark adaptation mesopic
• workload measurement

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model