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目的:探究个人的购物出行模式随时间的变化差异性。探讨个人在星期中的天变化,量化多样化的特征和识别个体间的异质性。创新点:1.使用连续6周的数据进行动态交通行为分析;2.建立二元逻辑回归模型,识别个体间购物出行模式的异质性。方法:1.通过描述性统计分析,解析不同群体之间的购物行为的差异、每周购物天数的分布以及每周每人购物天数的标准偏差(表1、图1和2);2.通过回归模型分析,得出解释变量对购物地点和频率的影响结论(表2和3);3.构建二元逻辑回归模型,识别个体间购物出行模式的异质性,找出具有常规购物行为的群体(表4)。结论:1.低收入和卡尔斯鲁厄居住地对日常购物目的地的多样性呈正影响关系,而全职工作和男性对日常购物目的地的多样性呈负影响关系;2.连续6周的日常购物出行次数受到35~44岁的年龄范围、2500~2999德国马克的家庭月收入以及市中心居住地区的积极影响;3.购物地点和频率的多样性是随着个人属性和居住地点以系统的方式变化。
Objective: To explore the individual differences in shopping travel patterns over time. Explore individual day changes in the week, quantify the diversity of features and identify individual heterogeneities. Innovative points: 1. Using continuous 6 weeks of data for dynamic traffic behavior analysis; 2. To establish a binary logistic regression model to identify the heterogeneity of individual shopping travel patterns. Methods: 1. Descriptive statistical analysis was used to analyze the differences in shopping behavior between different groups, the distribution of weekly shopping days and the standard deviation of weekly shopping hours per person (Table 1, Figures 1 and 2); 2. Passing (Table 2 and 3); 3.To build a binary logistic regression model to identify the heterogeneity of shopping travel patterns among individuals, to find out whether there is a regular shopping behavior Groups (Table 4). Conclusion: 1.Low income and the place of residence in Karlsruhe have a positive impact on the diversity of daily shopping destinations, while full-time employment and men have a negative impact on the diversity of daily shopping destinations; 2.Daily 6 weeks The number of shopping trips is influenced by the age range of 35-44 years old, the family monthly income of 2500- 2999 deutsche marks and the positive impact of living in the city center; 3. The diversity of shopping locations and frequencies varies with personal attributes and place of residence in a systematic The way to change.