한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제22권1호
  • /
  • Pages.28-38
  • /
  • 2019
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  • 1226-9719(pISSN)
  • /
  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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스마트카드 자료를 활용한 대중교통 승객의 통행목적 추정

Estimating the Trip Purposes of Public Transport Passengers Using Smartcard Data

  • 전인우 (서울시립대학교 공간정보공학과) ;
  • 이민혁 (서울시립대학교 공간정보공학과) ;
  • 전철민 (서울시립대학교 공간정보공학과)
  • 투고 : 2019.03.04
  • 심사 : 2019.03.18
  • 발행 : 2019.03.31
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초록

스마트카드 자료에는 개별 승객의 대중교통 이용기록이 저장되고, 이를 활용하면 정류장별, 시간대별 통행수요를 분석할 수 있다. 다만 스마트카드 자료에는 통행목적이 기록되어 있지 않기 때문에 통근, 통학, 여가 등의 목적별 수요는 설문조사 자료를 기반으로 추정되고 있다. 하지만 설문조사 자료에는 일부 표본의 통행만 기록되어 있어 전반적인 대중교통 통행수요를 추정하는데 한계가 있다. 만약 스마트카드 자료에서 통행목적을 추정할 수 있다면, 전수조사에 가까운 통행목적별 대중교통 수요에 대한 분석이 가능하다. 이에 본 연구에서는 스마트카드 자료에 기록된 승객의 O-D 통행빈도, 체류 시간, 출발 시각 등을 고려하여 통근, 통학, 귀가의 통행목적을 추정하는 방법론을 제시한다. 결과적으로 제시한 방법론을 적용하여 승객 중 근로자와 대학생을 분류하였다. 제시한 방법론의 검증으로는 가구통행실태조사 자료의 목적별 통행패턴과 본 연구를 통해 추정한 목적별 통행패턴을 비교하였다.

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.

키워드

GRJBBB_2019_v22n1_28_f0001.png 이미지

FIGURE 1. Estimating residence zones and activity zones of passengers

GRJBBB_2019_v22n1_28_f0002.png 이미지

FIGURE 3. The cumulative distribution of duration for commute and school in survey data

GRJBBB_2019_v22n1_28_f0003.png 이미지

FIGURE 4. Spatial distribution of estimated population of residence zones (a) and activity zones (b)

GRJBBB_2019_v22n1_28_f0004.png 이미지

FIGURE 5. Comparing deparutre times of smartcard data and survey data

GRJBBB_2019_v22n1_28_f0005.png 이미지

FIGURE 6. Comparing the travel times of between smartcard data and survey data

GRJBBB_2019_v22n1_28_f0006.png 이미지

FIGURE 2. Determining university zones

TABLE 1. An example of smartcard data

GRJBBB_2019_v22n1_28_t0001.png 이미지

TABLE 2. The variables in the algorithm

GRJBBB_2019_v22n1_28_t0002.png 이미지

TABLE 3. Rules for estimating trip purposes

GRJBBB_2019_v22n1_28_t0003.png 이미지

TABLE 4. Smartcard data with trip purposes estimated

GRJBBB_2019_v22n1_28_t0004.png 이미지

TABLE 5. Comparison of travel ratio by trip purpose between smartcard data and survey data

GRJBBB_2019_v22n1_28_t0005.png 이미지

참고문헌

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