Characteristics and identification of risky driving behaviors in expressway tunnels based on behavior spectrum

Abstract

Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments, which can easily lead to drivers developing constrained and irritable psychology. This may result in risky behaviors, e.g., speeding and fatigued driving. Previous research on tunnel driving behaviors mainly focuses on visual factors, neglecting the impacts of nonstationary time-series combined parameters on risky driving. Firstly, 30 drivers were recruited to carry out the real test. Then, based on the evolution of time series, drawing inspiration from the concept of lineage in biology, and considering multiple driving performance indicators, driving behavior chains and the feature spectrum were constructed. The characteristics of the behavior spectrum were divided into six groups: electroencephalogram, heart rate, eye movement, speed, steering, and car-following behaviors. Subsequently, the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors. The study deeply explored the inducing mechanism, hidden patterns, and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’ attributes. Finally, the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting (AdaBoost) and random forest (RF) algorithms. The synthetic minority over-sampling technique (SMOTE) and adaptive synthetic sampling (ADASYN) were employed for oversampling. The results indicate that the ADASYN-RF algorithm outperformed others, achieving a precise recall rate area under the curve (AUPRC) of 0.978 when using the spectral radius of the speed and steering groups as input variables. These findings offer theoretical guidance for developing tunnel traffic safety strategies.