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2016 Vol.34, Issue 5 Preview Page
Inferring the Transit Trip Destination Zone of Smart Card User Using Trip Chain Structure

통행사슬 구조를 이용한 교통카드 이용자의 대중교통 통행종점 추정

Kangwon SHIN1*
신 강원1*
1School of Civil, Urban, and Environmental Engineering, Kyungsung University
1경성대학교 건설환경도시공학부
*교신저자. *Corresponding Author. 
October 2016. pp. 437-448
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Abstract

Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip’s origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman’s rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.

Keywords
missing alighting stop
public transport
smart card
trip chain
trip destination

본 연구는 선행연구에서 제시하고 있는 통행기점 정보만을 제공하고 있는 불완전한 대중교 통카드 자료로부터 대중교통 통행의 종점을 통행사슬 구조를 이용하여 추정할 수 있는 모형의 국내 자료 적용 가능성을 살펴보고 모형 적용 결과를 제시하였다. 이를 위해 본 연구는 부산에 서 2014년 10월 주중에 수집된 선불 교통카드 승․하차 태그 원시자료 1,846,252건을 대상으로 하루 동안 한 대중교통 이용자가 발생시킨 일련의 통행들을 시․공간적으로 연계시켜 통행사슬을 형성하고, 대중교통 이용자의 결측 종점을 연속된 다음 통행의 승차지점 또는 최초 승차지점이 속한 교통존으로 추 정하였다. 모형 검증을 위해 대중교통 통행종점이 관측된 자료에 모형을 적용한 결과 실제 통행종점과 추정 통행종 점의 일치도는 82.4%로 나타났으며 이 때 통행종점으로 추정된 정류장과 실제 하차 정류장간 거리의 오차는 최소 가 되는 것으로 나타나 제안모형의 유용성은 높은 것으로 분석되었다. 통행사슬 구조를 이용한 통행종점 추정 모형 을 종점결측 통행에 적용했을 때 종점결측 통행의 비율은 적용 전 71.40%(718,915통행)에서 21.74%(218,907통 행)로 감소하였으며 종점추정이 불가한 218,907통행의 대부분은 모형 적용이 불가한 일일 통행횟수 ‘1회’인 통행 (169,359통행, 77.37%)인 것으로 나타났고, 일일 통행횟수가 ‘2회 이상’인 통행의 종점결측 비율은 69.56%에서 모 형 적용 후 6.27%로 크게 감소하였다. 한편 통행종점 추정 모형 적용에 따른 존간 통행 및 존내 통행분포의 변화를 비교하기 위해 순위상관계수 및 카이제곱 적합도 검정을 수행하였으며, 분석 결과 통행종점 추정 모형 적용에 따라 각 중존별 통행량의 순위는 변화하지 않으나 통행량 분포는 유의한 변화를 보였다. 따라서 통행사슬 구조를 이용한 교통카드 이용자의 통행종점 추정 모형 적용은 통행종점이 결측된 불완전 대중교통카드 자료가 수집되고 있는 도시 의 대중교통 통행패턴을 보다 현실적으로 반영할 수 있게 도움을 줄 것으로 판단된다.

키워드
하차결측
대중교통
교통카드
통행사슬
통행종점
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 34
  • No :5
  • Pages :437-448
  • Received Date : 2016-05-31
  • Revised Date : 2016-08-11
  • Accepted Date : 2016-10-27
  • DOI :https://doi.org/10.7470/jkst.2016.34.5.437
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