Gi-Hyoug Cho
UNIST 건설환경공학부 · 사회과학
Gi-Hyoug Cho 교수의 연구실은 도시 환경과 이동 행동 간의 상호작용을 중심으로 연구를 진행합니다. 특히 지역 규모의 환경 요소가 보행 및 대중교통 이용 행동에 미치는 영향을 분석하며, GPS 및 에이전트기반 시뮬레이션 모델을 활용한 정밀한 행동 분석 기법을 개발하고 있습니다. 또한, 재난 대비 대피 시뮬레이션과 자전거 공유 제도의 대중교통 통합 효과 등 실생활 문제 해결을 위한 정책 기반 연구도 활발히 수행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
In examining the association between environmental exposures and walking, conducting research on a neighbourhood scale has been the dominant approach whereas the association of the regional-scale environment with behaviours has rarely been explored. Because regional location and neighbourhood built environment attributes are likely to be correlated, the findings in neighbourhood-scale studies may be biased. In contrast to existing literature, this study is based on the assumption that a neighbou
Large-scale chemical accidents that occur near areas with large populations can cause significant damage not only to employees in a workplace but also to residents near the accident site. Despite the increasing frequency and severity of chemical accidents, few researchers have argued for the necessity of developing scenarios and simulation models for these accidents. Combining the TRANSIMS (Transportation Analysis and Simulation System) agent-based model with the ALOHA (Areal Location of Hazardo
Understanding how bike-share interacts with public transit is vital to determining the potential benefits of bike-share on the existing urban transportation system. This study examines the effects of bike-share users’ socio-demographics and trip features on whether bike-share users integrate or substitute public transit by conducting a questionnaire survey of Seoul’s bike-share users. The multinomial logistic model (MLM) was used for the statistical analysis. Our results showed that the bike-sha
While travel diaries are widely used to investigate walking behavior, the emergence of portable GPS units provides an innovative approach to characterizing walking behavior. This study compares the number and duration of daily walking trips reported in travel diaries with data extracted from a portable GPS unit and identified as the same walking trips. The study had two phases: (1) We used 35 person-days of travel data to determine the best algorithm for identifying walking trips from GPS data.
Travel behavior researchers have dominantly explored the influence of increase in development densities with mixed pattern of land-uses, and investment in infrastructures related to public transit toward more sustainable-transportation policies. However, little has been known about the long-term interdependencies between people’s decisions on travel behavior and individual biographies relating to residential relocation and habitual behavior over a longer time period. To fill this gap, the presen