Accommodating preference heterogeneity in commuting mode choice: an empirical investigation in Shaoxing, China
被引:5
|
作者:
Fu, Xuemei
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机构:
Shanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R ChinaShanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China
Fu, Xuemei
[1
]
Juan, Zhicai
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机构:
Shanghai Jiao Tong Univ, Dept Management Sci, Antai Coll Econ & Management, Shanghai, Peoples R ChinaShanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China
Juan, Zhicai
[2
]
机构:
[1] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Management Sci, Antai Coll Econ & Management, Shanghai, Peoples R China
Mode choice;
perceived attributes;
latent class model;
preference heterogeneity;
China;
LATENT CLASS MODEL;
MARKET-SEGMENTATION;
TRAVEL BEHAVIOR;
MIXED LOGIT;
D O I:
10.1080/03081060.2017.1300240
中图分类号:
U [交通运输];
学科分类号:
08 ;
0823 ;
摘要:
A latent class model is developed to accommodate preference heterogeneity across commuters with respect to their mode choice between electric bike, private car, and public bus within the context of China. A three-segment solution - electric bike individuals', private car addicts', and public bus enthusiasts' - is identified, each characterized by heterogeneous preferences regarding specific mode attributes and unique socio-demographic profile. The choice model confirms the determinative effects of perceived alternative attributes on commuting mode choice, while the traditionally used objective attributes - travel time and cost - are found to have relatively small influences. The membership model provides solid explanations for these segment-specific preferences. This study provides a better understanding of the nature of mode choice behavior, which can be useful for strategies tailored to a specific segment in order to promote the use of sustainable transport modes.
机构:
Institute of Food and Nutrition Development, Ministry of Agriculture and Rural AffairsInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs
HUANG Ze-ying
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机构:
Alec ZUO
SUN Jun-mao
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机构:
Institute of Food and Nutrition Development, Ministry of Agriculture and Rural AffairsInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs
SUN Jun-mao
GUO Yan-zhi
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机构:
Institute of Food and Nutrition Development, Ministry of Agriculture and Rural AffairsInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs
机构:
Birmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
UK & Cairo Univ Business Sch, Cairo, EgyptBirmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
El-Gohary, Hatem
Edwards, David
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机构:Birmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
Edwards, David
Eid, Riyad
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机构:
Birmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
UAE Univ, Fac Business & Econ, Al Ain, U Arab EmiratesBirmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
Eid, Riyad
Huang, Juanling
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机构:
Birmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, EnglandBirmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England
机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China
Wang, Xiaoquan
Shao, Chunfu
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机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China
Shao, Chunfu
Yin, Chaoying
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机构:
Nanjing Forestry Univ, Coll Automobile & Traff Engn, Nanjing, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China
Yin, Chaoying
Dong, Chunjiao
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机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China