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

Yu Chen, Mahdi Aghaabbasi, Mujahid Ali, Sergey Anciferov, Linar Sabitov, Sergey Chebotarev, Karina Nabiullina, Evgeny Sychev, Roman Fediuk and Rosilawati Zainol
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Yu Chen: School of Architecture, Hunan University, Changsha 410012, China
Mahdi Aghaabbasi: Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Malaysia
Mujahid Ali: Department of Civil and Environmental Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
Sergey Anciferov: Department of Mechanical Equipment, Belgorod State Technological University Named after V.G. Shukhov, 308012 Belgorod, Russia
Linar Sabitov: Department of Structural and Design Engineering, Kazan Federal University, 420008 Kazan, Russia
Sergey Chebotarev: Moscow State University of Technologies and Management (FCU), 109004 Moscow, Russia
Karina Nabiullina: Department of Structural and Design Engineering, Kazan Federal University, 420008 Kazan, Russia
Evgeny Sychev: Department of Mechanical Equipment, Belgorod State Technological University Named after V.G. Shukhov, 308012 Belgorod, Russia
Roman Fediuk: Far Eastern Federal University, 690922 Vladivostok, Russia
Rosilawati Zainol: Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Malaysia

Sustainability, 2021, vol. 14, issue 1, 1-26

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; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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