교통카드 트랜잭션 데이터베이스에서 통행 패턴 탐사와 통행행태의 분석
Mining Trip Patterns in the Large Trip-Transaction Database and Analysis of Travel Behavior
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이 논문은 대용량의 교통카드 트랜잭션 데이터베이스에서 통행패턴을 찾아내는 데이터 마이닝 방법의 개발에 초점을 두었으며, 결과로 도출된 통행패턴의 공간적 특징과 시점 간 차이를 분석하였다. 특히 대용량 데이터베이스에서 요구하는 지식을 효과적으로 발굴해 내는 순회 패턴 탐사법을 원용하여 통행패턴분석에 적절한 데이터 마이닝 알고리즘을 개발하여 2004년 이후 2006년 까지 3개년의 하루 교통카드 자료에 적용하였다. 또한 통행 순차 데이터베이스에서 오전 출근 시간대, 낮 시간대, 저녁 퇴근 시간대의 출발 정류장과 도착 정류장에 대한 통행 수요를 산출하여 시간대별 통행패턴의 공간 특징을 분석하였다.
Abstract
The purpose of this study is to propose mining processes in the large trip-transactiondatabase of the Metropolitan Seoul area and to analyze the spatial characteristics of travel behavior.For the purpose. this study introduces a mining algorithm developed for exploring trip patternsfrom the large trip-transaction database produced every day by transit users in the MetropolitanSeoul area. The algorithm computes trip chains of transit users by using the bus routes and a graphof the subway stops in the Seoul subway network. We explore the transfer frequency of the transitusers in their trip chains in a day transaction database of three different years. We find the numberof transit users who transfer to other bus or subway is increasing yearly. From the trip chains of thelarge trip-transaction database, trip patterns are mined to analyze how transit users travel in thepublic transportation system. The mining algorithm is a kind of level-wise approaches to findfrequent trip patterns. The resulting frequent patterns are illustrated to show top-ranked subwaystations and bus stops in their supports. From the outputs, we explore the travel patterns of threedifferent time zones in a day. We obtain sufficient differences in the spatial structures in the travelpatterns of origin and destination depending on time zones. In order to examine the changes in thetravel patterns along time, we apply the algorithm to one day data per year since 2004. The resultsare visualized by utilizing GIS, and then the spatial characteristics of travel patterns areanalyzed.The spatial distribution of trip origins and destinations shows the sharp distinction among
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