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Analyzing Transfer Commuting Attitudes Using a Market Segmentation Approach

Jiao Ye (), Jun Chen (), Hua Bai () and Yifan Yue ()
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Jiao Ye: School of Transportation, Southeast University, Nanjing 210096, China
Jun Chen: School of Transportation, Southeast University, Nanjing 210096, China
Hua Bai: China Design Group Co., Ltd., Nanjing 210014, China
Yifan Yue: School of Transportation, Southeast University, Nanjing 210096, China

Sustainability, 2018, vol. 10, issue 7, 1-16

Abstract: Commuting by transfer in the public transit network is a green travel choice compared to private cars which should be encouraged when direct transit lines cannot take the commuters to their destinations. Therefore, transfer commuting attitudes are important for finding appropriate ways to attract more transfer commuters. Firstly, since attitudes are usually unobserved, a combined revealed preference (RP) and stated preference (SP) survey was conducted in Nanjing, China to obtain the observed attitudinal variables. Then the market segmentation approach including the factor analysis, the structural equation modelling (SEM) model and the K-means clustering method was used to identify the underlying attitudinal factors and variables and analyze the interrelationship between them. Six segments were identified by four key factors including the willingness to transfer, the sensitivity to time, the need for flexibility and the desire for comfort. The sensitivity to time is the most important factor for commuters influencing their willingness to transfer. The socio-economic features of each segment were also analyzed and compared. The result shows that socio-economic features have a great impact on the willingness to transfer. Corresponding policy and strategy implications to increase transfer commuting proportion were finally proposed.

Keywords: transfer commuting; attitudinal survey; market segmentation; structural equation modelling (SEM); K-means clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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