Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques

Aghaabbasi, M.; Shekari, Z. A.; Shah, M. Z.; Olakunle, O.; Armaghani, D. J.; Moeinaddini, Mehdi

doi:10.1016/j.tra.2020.04.013

Article (Scientific journals)

Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques

Aghaabbasi, M.; Shekari, Z. A.; Shah, M. Z. et al.

2020 • In Transportation Research. Part A, Policy and Practice, 136, p. 262-281

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https://hdl.handle.net/2268/249943

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10.1016/j.tra.2020.04.013

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Keywords :

Bayesian Network; Off-campus university students; Random Forest; Ride-sourcing use frequency; Bayesian networks; Decision trees; Learning systems; Leisure; Students; Built environment; Machine learning techniques; Neighbourhood; Public universities; Related factors; Safety perception; Survey techniques; University students; Random forests; Bayesian analysis; Malaysia

Abstract :

[en] This study used a survey technique to investigate factors that motivate the adoption and the usage frequency of ride-sourcing among students in a Malaysia public university. Two of the most broadly used machine learning techniques, Random Forest technique and Bayesian network analysis were applied in this study. Random Forest was employed to establish the relationship between ride-sourcing usage frequency and students' socio-demographic related factors, built environment considerations, and attitudes towards ride-sourcing specific factors. Random Forest identified 10 most important factors influencing university students’ use of ride-sourcing for different travel purposes, including study-related, shopping, and leisure travel. These important predictors were found to be indicators of the target variables (i.e., ride-sourcing usage frequency) in Bayesian network analysis. Bayesian network analysis identified the students' age (0.15), safety perception (0.32), and neighbourhood facilities in a walkable distance (0.21) as the most important predictors of the use of ride-sourcing among students to get to school, shopping, and leisure, respectively. © 2020 Elsevier Ltd

Disciplines :

Special economic topics (health, labor, transportation...)
Civil engineering
Regional & inter-regional studies

Author, co-author :

Aghaabbasi, M.; Centre for Innovative Planning and Development, Department of Urban and Regional Planning, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia

Shekari, Z. A.; Centre for Innovative Planning and Development, Department of Urban and Regional Planning, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia

Shah, M. Z.; Centre for Innovative Planning and Development, Department of Urban and Regional Planning, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia

Olakunle, O.; Department of Urban and Regional Planning, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia

Armaghani, D. J.; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

Moeinaddini, Mehdi ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité

Language :

English

Title :

Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques

Publication date :

2020

Journal title :

Transportation Research. Part A, Policy and Practice

ISSN :

0965-8564

eISSN :

1879-2375

Publisher :

Elsevier Ltd

Volume :

136

Pages :

262-281

Peer reviewed :

Peer Reviewed verified by ORBi

Funders :

MOHE - Ministry of Higher Education

Available on ORBi :

since 06 August 2020

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