How Sustainable Is People\'s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships - 成果

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How Sustainable Is People's Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships

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成果类型：

期刊论文

作者：

Tang, Panyu;Aghaabbasi, Mahdi;Ali, Mujahid;Jan, Amin;Mohamed, Abdeliazim Mustafa;...

通讯作者：

Tang, P.

作者机构：

[Tang, Panyu] ChengDu Jincheng Coll, Chengdu 611731, Peoples R China.

[Aghaabbasi, Mahdi] Univ Malaya, Fac Built Environm, Dept Urban & Reg Planning, Ctr Sustainable Urban Planning & Real Estate SUPR, Kuala Lumpur 50603, Wilayah Perseku, Malaysia.

[Ali, Mujahid] Univ Teknol Petronas, Dept Civil & Environm Engn, Seri Iskandar 32610, Perak, Malaysia.

[Jan, Amin] Univ Malaysia Kelantan, Fac Hosp Tourism & Wellness, City Campus, Kota Baharu 16100, Kelantan, Malaysia.

[Mohamed, Abdeliazim Mustafa] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Alkharj 16273, Saudi Arabia.

通讯机构：

ChengDu Jincheng College, Chengdu, China

语种：

英文

关键词：

Bayesian network algorithm;complex relationship;sustainable travel to public transit stations;work trip

期刊：

Sustainability

ISSN：

2071-1050

年：

2022

卷：

期：

DOI：

10.3390/su14073989

机构署名：

本校为第一机构

摘要：

Several previous studies examined the variables of public-transit-related walking and privately owned vehicles (POVs) to go to work. However, most studies neglect the possible nonlinear relationships between these variables and other potential variables. Using the 2017 U.S. National Household Travel Survey, we employ the Bayesian Network algorithm to evaluate the non-linear and interaction impacts of health condition attributes, work trip attributes, work attributes, and individual and household attributes on walking and privately owned vehicle...

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