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All Journal Civil and Sustainable Urban Engineering
Mahdi Aghaabbasi
Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Malaysia
Civil Engineering, Building, Construction & Architecture Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Investigating the Use of Active Transportation Modes Among University Employees Through an Advanced Decision Tree Algorithm Mahdi Aghaabbasi; Muhammad Zaly Shah; Rosilawati Zainol
Civil and Sustainable Urban Engineering Vol. 1 Iss. 1 (2021)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1251.698 KB) | DOI: 10.53623/csue.v1i1.28

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.
Co-Authors Muhammad Zaly Shah Rosilawati Zainol