Connecting road environment features and driver glance behavior in the macro level: Surrounding vehicle patterns, traffic density, and driver eye-glance behaviors

Although integration of environment and driver information can be achieved at both micro- and macro- levels with different benefits towards driving safety, most studies focus only on the micro-level integration by coupling individual external environment events and driver responses. In the macro level, however, it is more important to understand overall effects of environment features on driver behavior, and their combined effects on driving safety. Based on some previous findings on the significant effects of driver glance behavior on crash risk and the prominent moderating effects of traffic density levels on this relationship, this paper tries to use surrounding vehicle patterns to classify traffic density levels and study the direct effects of traffic density levels on driver glance behavior. The datasets used for analysis are based on VTTI 100-car study. After proposing the measures of surrounding vehicle patterns, the classification of traffic density is completed using support vector machine (SVM). The results show that, although it is difficult to classify four traffic density levels based on the proposed surrounding vehicle patterns, the predication accuracy for two or three traffic density levels is good. Also, significant effects of traffic density on driver glance behaviors to the vehicle mirrors are identified.