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Research on the Relationship between the Individual Characteristics of Electric Bike Riders and Illegal Speeding Behavior: A Questionnaire-Based Study

Changxi Ma (), Jibiao Zhou (), Dong Yang () and Yuanyuan Fan ()
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Changxi Ma: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Jibiao Zhou: College of Transportation Engineering, Tongji University, Shanghai 200092, China
Dong Yang: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Yuanyuan Fan: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Sustainability, 2020, vol. 12, issue 3, 1-1

Abstract: To examine the relationship between electric bike riders’ individual characteristics and their riding speed, this paper obtained 350 valid survey responses from e-bike riders using an on-site sampling survey method. Using the non-aggregate theory, we take the individual attributes of the rider’s age, driving age, personality, and corrective vision as potential influencing factors. The metric model of the influencing factors of the rider’s personal characteristics on riding speed is established, and we analyze the sensitivity of many influencing factors by using the theory of elasticity. The results show that the absolute value of the elasticity value corresponding to the rider’s gender, age, corrected visual acuity, and other factors is less than 1, which indicates that the above factors have no flexibility regarding the rider’s riding speed selection behavior. However, in four selection intervals, the elasticity values of the rider’s education level are 1.577, 2.484, 1.810, and 1.667; those of their driving age are −1.537, −2.061, −1.547, and −1.606, and those of their riding proficiency are 3.302, 12.038, 10.370, and 11.177, which indicate that the three factors of rider’s education level, driving age, and riding proficiency have a significant impact on the riding speed choice behavior. The finding of the study is helpful for the relevant government departments to formulate more accurate classified intervention measures, and effectively prevent the occurrence of illegal speeding behavior.

Keywords: electric bike rider; personal characteristics; the non-aggregate theory; riding speed; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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