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Investigating the Use of Active Transportation Modes Among University Employees Through an Advanced Decision Tree Algorithm

by Mahdi Aghaabbasi 1 , Muhammad Zaly Shah 2 , Rosilawati Zainol 1
1 Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Malaysia
2 Center for Innovative Planning and Development (CIPD), Department of Urban and Regional Planning, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Malaysia

SUBMITTED: 15 September 2021; ACCEPTED: 28 October 2021; PUBLISHED: 15 November 2021

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Abstract

Abstract

Now more than ever, the health and economic benefits of active transportation (AT) are evident and several planning efforts and programs are particularly targeted at improving active transportation options for different populations, such as students and seniors. Administrative employees at universities received less attention in the literature than other population groups.This population spends a lot of time doing sedentary activities and behaviors during their working time. Thus, the present study used a C5 decision tree to examine the usage of university employees’ AT modes when they are out of campus to get to work, shopping, and leisure. The effects of the sociodemographic and living environment of employees on their AT mode choice were also examined. According to the results, walking was the most frequently used mode to get to work and leisure and public transport was the most frequently used mode to get to shopping. Transit station conditions (25), sidewalk availability and coverage (36), and bike path availability and coverage (30) were the most important factors in the use of AT modes by employees to get to work, shop, and leisure, respectively. Furthermore, several decision rules were extracted from the C5 tree, which included combinations of multiple factors.

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Keywords: active transportation; mode choice; university employees; trip purposes; C5

Creative Commons Attribution 4.0 International (CC BY 4.0) License
© 2021 Mahdi Aghaabbasi, Muhammad Zaly Shah, Rosilawati Zainol. This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Aghaabbasi, M., Zaly Shah, M., & Zainol, R. (2021). Investigating the Use of Active Transportation Modes Among University Employees Through an Advanced Decision Tree Algorithm. Civil and Sustainable Urban Engineering, 1(1), 26–49. https://doi.org/10.53623/csue.v1i1.28
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