Travel Pattern Extraction from Smart Card Data using Data Polishing

被引：0

作者：

Hosoe, Mio ^{[1
]}

Kuwano, Masashi ^{[1
]}

Moriyama, Taku ^{[1
]}

Miyazaki, Kosuke ^{[2
]}

Ito, Masaki ^{[3
]}

机构：

[1] Tottori Univ, Dept Management Social Syst & Civil Engn, Tottori, Japan

[2] Kagawa Natl Coll Technol, Dept Civil Engn, Mitoya, Kagawa, Japan

[3] Univ Tokyo, Inst Ind Sci, Tokyo, Japan

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年

关键词：

data polishing; smart card; high order data; travel pattern; TUCKER TENSOR DECOMPOSITION; BEHAVIOR;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the development of [CT (Information and Communication Technology), interest in using the large amount of accumulated data for traffic policy planning has been increasing. In recent years, data polishing has been proposed as a new methodology for big data analysis. Data polishing is one of the graphical clustering methods. This method can be used to extract patterns that are similar or related to each other by clarifying the cluster structures in the data. The purpose of this study is to reveal travel patterns of railway passengers by applying data polishing to smart card data collected in Kagawa Prefecture, Japan. This study uses 9,008,709 data points collected during the 15 months from December 1st, 2013 to February 28th, 2015. This data set includes such information as trip histories and types of passengers. The study uses the data polishing method to cluster 4,667,520 combinations of information about individual rides: day of the week, time of day, passenger type, origin station, and destination station. As a result, 127 characteristic travel patterns were specified from those combinations.

引用

页码：3563 / 3572

页数：10

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