On New Measures for Detection of Data Quality Risks in Mobility Panel Surveys
On New Measures for Detection of Data Quality Risks in Mobility Panel Surveys
复制标题
流动性追踪调查数据质量风险检测新措施
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
P. Vortisch
中科院分区：
文献类型：
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作者：
M. Wirtz;Tatjana Streit;Bastian Chlond;P. Vortisch
多天和多周期的小组调查是评估个人旅行行为变化的最先进方法。虽然这些调查对交通规划者很重要，但对参与者来说相当耗时，因此可能导致错误和有偏见的流动数据。数据质量的差异极大地影响了对流动数据的统计分析以及使用流动数据作为制定活动计划基础的常见微观旅行需求模型。作为对流动性关键数据加权偏差的著名方法的补充，本文重点研究了检测个人旅行日记之间数据质量差异的方法。这些质量指标解决了调查不同阶段的动机丧失问题。基于这些新的质量措施的分类方法有助于发现错误数据和可能的辍学。研究结果可能有助于通过提前单独解决潜在辍学者并提高他们的积极性来减少辍学。质量措施是用德国流动性小组的最新数据进行测试的。对于年龄超过60岁的参与者，质量措施显示出良好的分类结果的准确性，但对于年龄小于35岁的参与者的质量措施是无效的，在确定辍学。这种个人的方法结合部分检查和旅行日记的校正可能是有用的微观旅行需求建模的基础上外部活动链。
Multiday and multiperiod panel surveys are state-of-the-art methods to assess changes in individual travel behavior. Though important for transport planners, these surveys are rather time-consuming for participants and therefore might lead to erroneous and biased mobility data. Variability in the data quality significantly affects statistical analyses of mobility figures as well as common microscopic travel demand models that use the mobility data as the basis for generating activity plans. Supplementary to the well-known approach of weighting biases in key figures of mobility, this paper focuses on methods for detecting data quality differences between individual travel diaries. These quality measures address aspects of motivation loss at different stages of the survey. A classification approach based on these new quality measures helps to detect erroneous data and possible dropouts. The results might help reduce dropouts in general by addressing the potential dropouts individually in advance and boosting their motivation. Quality measures are tested with recent data from the German Mobility Panel. For participants older than 60 years of age, the quality measures show good classification results in regard to accuracy, but for participants younger than 35 years of age the quality measures are not effectual in identifying dropouts. Such an individual approach combined with the partial inspection and correction of travel diaries may be useful for microscopic travel demand modeling based on external activity chains.