A method for evaluating the safety of freeway tunnel sections based on driving comfort - a naturalistic driving study.

OBJECTIVE: Driving comfort is crucial for tunnel safety because tunnel sections on freeways often introduce significant environmental changes that can compromise comfort and increase the risk of traffic accidents. This study aimed to quantitatively evaluate the driving comfort in tunnel sections and its implications for safety management. METHODS: Four indicators were used to assess the driving comfort: heart rate growth rate (Hrgr), skin conductance response (SCR), speed, and acceleration. The CRITIC weighting method was employed to calculate a quantitative driving comfort score, and the presence and severity of discomfort were used to evaluate the safety of each tunnel area. In addition, the evaluation was based on a naturalistic test consisting of Hrgr, SCR, speed, and acceleration data. A total of 32 participants were recruited based on a web-based questionnaire screening process, after which they were tested while driving through 30 tunnel sections on the roadway. These 30 tunnels included 14 short (< 500 m), 12 medium (500-1,000 m), and 4 long (1,000-3,000 m) tunnels. RESULTS: The results revealed that the four selected indicators exhibited minimal multicollinearity and effectively captured the driving comfort. Among the indicators, SCR had the most significant contribution to the driving comfort score. Most drivers did not experience substantial discomfort while driving through tunnels. The area where drivers were most susceptible to discomfort was the middle zones of tunnels. However, drivers were more likely to experience strong discomfort in the outside exit, entrance, and middle zones of short, medium, and long tunnels, respectively. CONCLUSIONS: This study provides a comprehensive set of safety evaluation methods for tunnel sections on freeways, with a focus on quantifying the driving comfort. The findings provide theoretical support for freeway management personnel in implementing personalized controls in different tunnel areas with the aim of enhancing tunnel safety and mitigating the occurrence of traffic accidents.

Estimating mountainous freeway crash rate: Application of a spatial model with three-dimensional (3D) alignment parameters.

The road alignment is a three-dimensional (3D) curve in nature. In this study, we quantitatively examine the effect of 3D road alignment on traffic safety on mountainous freeways. Geometric parameters of 3D curvature and torsion in mathematics are derived to characterize the 3D road curve. Based on the coordination of different horizontal and vertical elements, 3D road alignment is divided into twelve types of combined alignment. For each alignment combination, the 3D curvature and torsion are calculated according to the differential geometry theory. Regarding crash statistical modeling, the Bayesian spatial Tobit (BST) model is developed to accommodate possible spatial correlation of traffic crashes among adjacent freeway segments. The Bayesian Tobit (BT) model is also built for comparison. A 118-km mountainous freeway associated road geometric features, traffic volume with three years of crash data is used as a case study. The result from the model comparison shows the BST model outperforms the BT model in terms of goodness-of-fit. Parameter estimation result for the BST model shows that the differences of average 3D curvature (and torsion) between adjacent segments have statistically significant effects on the crash rate of the segment, indicating it is necessary to consider three-dimensional alignment parameters in estimating mountainous freeway crash rate. Moreover, by comparing the predicted crash rate calculated by the BST model and the observed crash rate, the result shows the proposed BST model can provide a reliable prediction for freeway crash rates of different combined alignments. This study provides new insight on the effect of road geometric design on traffic safety but also deepens our understanding of spatial correlations in freeway crash modeling.