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Individual differences between various riders cause risky riding behaviors such as violations, taking the lead, negligence and error, and pushing the limits, resulting in a high incidence and high number of road accidents for the vulnerable road use group of electric bike riders. Therefore, the subcluster differential characteristics among riders were analyzed in terms of their riding confidence, risk perception, safety attitude, and basic attributes. The influences and formation mechanisms of risky riding behaviors among the subclusters of riders were also explored. First, the 573 riders were clustered into 4 types, action type, anxiety type, introversion type, and negative type, based on the E-bike Risky Riding Behavior Questionnaire (E-RBQ), factor analysis method, and K-means clustering. Second, a structural equation model of e-bike risky riding behavior (E-SEM) was established to explore the main influencing factors for the risky riding behavior of the 4 types of riders and the differences among them. Finally, risky riding behavior avoidance strategies for various types of riders were proposed. The findings showed that negligence and error (0.48) and take the lead behavior (0.44) of action types were significantly and positively influenced by judgment ability; violation behavior (-0.52) and take the lead behavior (-0.41) of anxiety types were significantly and negatively influenced by traffic rules; pushing the limits (-0.29) and take the lead behaviors (-0.31) of introversion types were significantly and negatively influenced by probability evaluation; and negligence and error (-0.43) and violation (0.37) of negative types were negatively and positively influenced by herd mentality. In particular, the overconfidence of the action and anxiety types in their own techniques and judgment ability may cause misjudgment of the surrounding area; the worry degree of the introversion type must be balanced effectively; and the negative type must control the degree of confidence in their judgment ability.

Formation Mechanisms and Clustering Differences in Risky Riding Behaviors of Electric Bike Riders