This study aimed to reveal the potential relationship between personal characteristics of e-bike riders and illegal occupation of motor vehicle lane. To this end, a questionnaire survey was conducted and 350 valid copies of responses were retrieved from the e-bike riders. Depending on the number of motor vehicle lanes occupied, the risky behavior of illegal occupation was divided into four intervals: intervals A, B, C, and D. The disaggregate theory has high adaptability to the analysis of individual traffic behavior. In this study, the multinomial logit model was used, and eight personal characteristics of e-bike riders were selected. The aforementioned four intervals were the four selection limbs, and a measurement model calculating the influence of personal characteristics on the behavior of illegal occupation was built. The theory of elasticity was employed to analyze the sensitivity degree of each influence factor. The results showed that the absolute values of elasticity of all tested influence factors, including age, educational level, and eye vision, were less than 1.000. However, on the four intervals, the elasticity of riders’ temperament was 1.203, 1.656, 1.554, and 1.355, respectively, and elasticity of riding proficiency was 2.782, 3.883, 3.453, and 2.932, respectively.

中文翻译：

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电动自行车车手的个人特征和非法车道占用行为

这项研究旨在揭示电动自行车骑手的个人特征与非法占用机动车道之间的潜在关系。为此，进行了问卷调查，并从电动自行车骑手处检索了350份有效答案。根据占用的机动车道数量，将非法占用的危险行为分为四个区间：区间A，B，C和D。分类理论对个人交通行为的分析具有很高的适应性。在这项研究中，使用了多项式logit模型，并选择了8个电动自行车骑手的个人特征。上述四个区间是四个选择肢，建立了计算个人特征对非法职业行为影响的测量模型。运用弹性理论分析各影响因素的敏感度。结果表明，所有测试的影响因素的弹性绝对值（包括年龄，学历和眼睛视觉）均小于1.000。然而，在这四个时间间隔上，骑手气质的弹性分别为1.203、1.656、1.554和1.355，骑行熟练度的弹性分别为2.782、3.883、3.453和2.932。