Drivers’ hazard perception analysis based on logistic regression and Cochran–Mantel–Haenszel test

Abstract

Current researches of hazard perception based on the conventional self-report, video scene, driving simulator experiments, and road studies all have their shortcomings. Accident interrogation record data not only have the benefits of the conventional self-report method (inexpensive and detailed), but also overcome the deficiencies of the self-report (impact of social desirability) to a great degree. In this article, the collision data, especially the accident interrogation record data on freeways in the City of Chongqing, China, are used to analyze the contributing factors to hazard perception, based on logistic regression and Cochran–Mantel–Haenszel test. The logistic method is used to study the correlation among these factors on hazard perception. In addition, the Cochran–Mantel–Haenszel test method is applied to factors that are not statistically significantly identified in logistic regression analysis. The results show that factors such as age, years of driving experience, gender, month, vehicle type, road alignment, and road surface have effects on hazard perception. The study results can be used to improve the drivers’ hazard perception abilities on freeways and can also help the highway administrators to formulate the related policies and regulations.