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

Tao Wang,Jin Yu,Yuzhi Chen,Sihong Xie

doi:10.1109/access.2021.3108039

Abstract

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.

Highlights

E-bikes are receiving increasing attention worldwide due to their small spatial footprint, high mobility and accessibility, and low cost of ownership and maintenance [1]
E-Structural equation modeling (SEM) MODEL VERIFICATION First, under the assumption of the full path relationship among the factors, the paths with insignificant path coefficients were eliminated based on the model results, and the model was rectified with the correction indices
With the aim of investigating the formation mechanism and the differences in risky riding behaviors of various e-bike riding clusters, E-bike Risky Riding Behavior Questionnaire (E-RBQ) data from Guilin and Nanning cities were obtained in this paper

Summary

Introduction

E-bikes are receiving increasing attention worldwide due to their small spatial footprint, high mobility and accessibility, and low cost of ownership and maintenance [1]. They are widely considered environmentally friendly [3], healthy, The associate editor coordinating the review of this manuscript and approving it for publication was Rashid Mehmood. Between 2013 and 2017, there were 56,200 accidents caused by e-bikes in China, resulting in 8431 deaths, 63,500 injuries, and 111 million yuan of direct property damage, where risky riding behaviors such as illegal lane occupation, running red lights, retrograde traffic, and speeding were the main causes of e-bike accidents [10]

Methods

Results

Conclusion