Fuzzy logic-based observation and evaluation of pedestrians’ behavioral patterns by age and gender

Abstract

Pedestrian behavior is affected by a multitude of factors such as age, gender, and operating conditions. However, traditional statistical analysis based on observed movements or questionnaire survey is unable to model decision-making process of each pedestrian. This study develops an innovative approach based on fuzzy logic to model the underlying cognitions and behavioral patterns of pedestrians as inferred from field observation in order to evaluate age and gender effect of pedestrians in crossing a signalized crosswalk and when jaywalking. Fuzzy sets and rules are created to model the relationship between human cognitions and decisions of an individual pedestrian. Through calibrating the membership functions of different age and gender groups, behavioral patterns of pedestrians are evaluated and compared. Different from most previous studies, both older and younger pedestrians are found to be less risk-taking than adult pedestrians. Moreover, significant gender difference is found only for cognitions of most hazardous conditions. Consistent with previous studies, it is seen that men have better cognitive skills than women at detecting hazardous situations. The findings from this study are useful to better design safe pedestrian crossing facilities. The fuzzy logic-based approach also provides an innovative way to simulate pedestrian movements in microscopic simulation models.