Hybrid Bayesian network models to investigate the impact of built environment experience before adulthood on students' tolerable travel time to campus: Towards sustainable commute behavior
Document Type
Article
Publication Date
1-1-2022
Abstract
This present study developed two predictive and associative Bayesian network models to forecast the tolerable travel time of university students to campus. This study considered the built environment experiences of university students during their early life-course as the main predictors of this study. The Bayesian network models were hybridized with the Pearson chi-square test to select the most relevant variables to predict the tolerable travel time. Two predictive models were developed. The first model was applied only to the variables of the built environment, while the second model was applied to all variables that were identified using the Pearson chi-square tests. The results showed that most students were inclined to choose the tolerable travel time of 0-20 min. Among the built environment predictors, the availability of residential buildings in the neighborhood in the age periods of 14-18 was the most important. Taking all the variables into account, distance from students' homes to campuses was the most important. The findings of this research imply that the built environment experiences of people during their early life-course may affect their future travel behaviors and tolerance. Besides, the outcome of this study can help planners create more sustainable commute behaviors among people in the future by building more compact and mixed-use neighborhoods.
Keywords
Tolerable travel time, University students, Built environment, Early life-course, Bayesian network, Mmachine learning
Divisions
BuiltEnvironment
Funders
Ministry of Science, ICT & Future Planning, Republic of Korea[075-15-2021-1333]
Publication Title
Sustainability
Volume
14
Issue
1
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND