인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Recent policy emphasis in the fields of urban and transport planning has been focused on reduction of vehicle miles traveled. Therefore, many empirical studies have tried to respectively identify either how mode choice private car is chosen against alternative transport ones or how much travel time by car is determined, not simultaneously both of them. However, such an unintentionally separated approach may give a bias on the estimation of actual private car travel time. This study is aimed at Identifying the unintentionally unrevealed impact of private car mode choice and its travel time in commuting, focusing on the multilevel determinant measures of individual, household, and residential area in the Seoul metropolitan region in Korea. Using the 2% sample raw data of the 2015 total population survey, the study employed the multilevel Tobit model to estimate private car commuting time with the latent variable of its mode choice against active modes. This is a combined model of the multilevel probit model for binomial mode choice and the multilevel linear regression model for private car commuting time. Comparing the two respective sub-models for mode choice and time of commuting, analysis results on the multilevel Tobit model demonstrate that there exist unintentionally but unrevealed effects in estimating actual private car commuting time in simultaneously considering individual behavior on mode choice and travel time. Such impacts are found in several determinant factors at all levels. The other conclusion coming from the analysis results is that reducing the probability of choosing private car on commuting is more effective than decreasing its commuting time from the perspective of actual consumed time.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2020-539-000420766
