Classification of similar travel behavior is essential for market segmentation research in geography and transportation science. Cluster analysis using sequence alignment measurement incorporates the sequential information embedded in activity-travel sequences. The resultant clusters are then typically associated with the relevant variables. However, although the sequences are clustered by similar sequential information, the summary of the clusters do not reflect the sequential information with scientific rigor. This is because of the non-numeric characteristics of the sequential information. The study aims to develop a method for finding a representative sequence (RepSeq) that better profiles the cluster of sequences. The suggested method employs a genetic algorithm to search for a sequence potentially closest to the centroid by computing the smallest sum of distances from the searched sequence to all sequences of the cluster using a sequence alignment method. The suggested method is also applied to the real sequence data of the use of transport modes in Seoul. The result provides useful information for cluster interpretation and the subsequent analyses.

中文翻译：

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使用遗传算法分析活动旅行序列集群

相似出行行为的分类对于地理和交通科学中的市场细分研究至关重要。使用序列比对测量的聚类分析结合了嵌入在活动-旅行序列中的序列信息。得到的聚类然后通常与相关变量相关联。然而，尽管序列是由相似的序列信息聚类的，但聚类的摘要并没有科学严谨地反映序列信息。这是因为顺序信息的非数字特性。该研究旨在开发一种寻找代表性序列 (RepSeq) 的方法，该方法可以更好地描述序列集群。建议的方法采用遗传算法，通过使用序列比对方法计算从搜索到的序列到簇的所有序列的最小距离总和，来搜索可能最接近质心的序列。建议的方法也适用于首尔交通方式使用的真实序列数据。结果为聚类解释和后续分析提供了有用的信息。