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
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 stu...
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| Veröffentlicht in: | Sustainability 2022-01, Vol.14 (1), p.325 |
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| creator | Chen, Yu Aghaabbasi, Mahdi Ali, Mujahid Anciferov, Sergey Sabitov, Linar Chebotarev, Sergey Nabiullina, Karina Sychev, Evgeny Fediuk, Roman Zainol, Rosilawati |
| description | 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. |
| doi_str_mv | 10.3390/su14010325 |
| format | Article |
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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.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14010325</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Bayesian analysis ; Behavior ; Built environment ; Chi-square test ; Child development ; College campuses ; Colleges & universities ; Forecasts and trends ; Investigations ; Malaysia ; Mathematical models ; Neighborhoods ; Polls & surveys ; Prediction models ; Residential areas ; Residential buildings ; Russia ; Sociodemographics ; Statistical tests ; Students ; Sustainability ; Travel ; Travel time ; United Kingdom ; University students ; Urban environments</subject><ispartof>Sustainability, 2022-01, Vol.14 (1), p.325</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Analysis Bayesian analysis Behavior Built environment Chi-square test Child development College campuses Colleges & universities Forecasts and trends Investigations Malaysia Mathematical models Neighborhoods Polls & surveys Prediction models Residential areas Residential buildings Russia Sociodemographics Statistical tests Students Sustainability Travel Travel time United Kingdom University students Urban environments |
| title | 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 |
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