Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Analysis of Traffic Card Big Data by Hadoop and Sequential Mining Technique

하둡과 순차패턴 마이닝 기술을 통한 교통카드 빅데이터 분석

  • Kim, Woosaeng (Computer Software Department, Kwangwoon University) ;
  • Kim, Yong Hoon (Computer Software Department, Kwangwoon University) ;
  • Park, Hee-Sung (Computer Software Department, Kwangwoon University) ;
  • Park, Jin-Kyu (Computer Software Department, Kwangwoon University)
  • Received : 2017.10.16
  • Accepted : 2017.12.27
  • Published : 2017.12.31
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Abstract

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

Keywords

References

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