Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method
Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method
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DOI：
10.1016/j.trc.2017.10.005
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发表时间：
2017-12
影响因子：
8.3
通讯作者：
Zhenhua Zhang;Qing He;Shanjiang Zhu
中科院分区：
文献类型：
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作者：
Zhenhua Zhang;Qing He;Shanjiang Zhu
本研究探讨利用社会媒体资料来推断纵向旅游行为的可能性。地理标记的社交媒体数据显示出一些独特的特征，包括位置聚合特征、距离分离特征和高斯分布特征。与传统的家庭出行调查相比，社交媒体数据成本更低，更容易获得，最重要的是可以在更长的观察期内监测个人的纵向出行行为特征。本文提出了一种基于序列模型的聚类方法，对高分辨率的Twitter位置进行分组，并提取Twitter位移。此外，本研究详细介绍了从Twitter中提取的位移的独特功能，包括Twitter用户的人口统计数据，以及优势和局限性。并与传统的家庭出行调查结果进行了比较，结果表明，该方法在利用出行距离分布、出行距离、出行持续时间和出行开始时间等信息推断出行行为方面具有一定的应用前景。在此基础上，人们还可以看到利用社交媒体来推断纵向旅行行为的潜力，以及大量的短距离Twitter位移。其结果将补充传统的出行调查，并支持大都市地区的出行行为建模。
This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.