X. Cai, S. Chen, S. Zhu, J. Lu

Pages: 21-32

Abstract
Human factors are contributing elements in rain-related accidents. However, drivers’ risk perception, as one of the most important human factors, has not been given much attention. Based on data collected from Freeway G15 in China, this paper attempts to establish a mathematical model to identify drivers’ risk perception under a variety of rainy weather conditions. Drivers’ questionnaire data (N=1216) were utilized to develop a multinomial logistic regression model to estimate impacts of rain intensity, location on the freeway, and traffic volume on the level of perceived risk by drivers. Results show that the three factors all have a positive relationship with drivers’ risk perception. Especially, rain intensity (Odds Ratio=22.321) and ramp and weaving area (Odds Ratio=24.576) would vastly increase the likelihood of catastrophic risk perception. Moreover, multinomial logistic regression models were also applied to identify how drivers’ gender and driven vehicle type could be associated with drivers’ risk perception under rainy weather conditions. Findings reveal that male drivers are more confident with their own maneuvering skills than female drivers when driving in the rain. And comparing with drivers owning small-sized vehicles, drivers owning large-sized vehicles are more careful and cautious on rainy days. The results could help traffic practitioners and professionals to better understand the safety implication of driving on rainy days and thus develop effective safety countermeasures and interventions.

Keywords: risk perception; multinomial logistic regression; odds ratio; rain; gender; vehicle type