The impact of attitudes and perceptions on travel mode choice and car ownership in a Chinese megacity: The case of Guangzhou

He, Thøgersen, 2017, in Research in Transportation Economics

doi:10.1016/j.retrec.2017.03.004
Location Guangzhou, China
Population General
Sample size 1000
Factor analysis type principal components, varimax rotation
Stepwise regression no
Removal of insignificant variables yes
Reviewed by LCM

Abstract

The objective of this research is to understand intentions to buy a car and how car-ownership, together with other personal, situational and attitudinal factors, influences travel mode choice in a Chinese megacity. A survey was carried out in Guangzhou, China to examine respondents’ car ownership and travel mode perceptions and choices, as well as personal and situational characteristics. A quota sampling procedure was used to select members of three different groups: car owners; no car but planning to buy one in the next 5 years – car intenders; and no car and not planning to buy one. We find that car ownership is the most important among included predictors of travel mode choice, which also depend on other personal and situational factors, but only indirectly by attitudinal factors. However, attitudinal factors have a strong impact on the intention to buy a car, while controlling for other personal and situational factors. Our results suggest that several policies can be further improved in China for greater effectiveness in reducing the car use of car owners and car intenders. © 2017 Elsevier Ltd

Factors

Variable Structure loading
Personal space (Comfort) 0.793
Enjoyment (Mode-liking) 0.7
Privacy (Comfort) 0.687
Ease of carrying personal belongings and luggage (Convenience) 0.624
Personal safety (e.g. crime) (Safety) 0.573
Vehicle cleanliness (Comfort) 0.57
Comfort level (Comfort) 0.546
Variable Structure loading
Time savings (Convenience) 0.763
Flexibility (e.g. travel time and route) (Convenience) 0.734
Speed (Convenience) 0.664
Convenience (Convenience) 0.663
Comfort level (Comfort) 0.547
Variable Structure loading
Safety from vehicle accident/collision (Safety) 0.848
Environmental friendliness (Environmentalism) 0.838
Congestion (Convenience) 0.834
Variable Structure loading
Protection from weather (Comfort) 0.749
Extra activity and travel opportunities (Res preference/perception) 0.722
Variable Structure loading
Reliability and punctuality (Convenience) 0.691
Accessibility (Convenience) 0.632
Variable Structure loading
Stress (Comfort) 0.751
Variable Structure loading
Status (symbol) (Social norms) nan
Cost (Cost) nan

Models

Dependent variable Likelihood that a person intends to buy a car
Model type Logistic regression
Sample size 652.0
R2 nan
Adjusted R2
Pseudo R2 (Cox & Snell) 0.9
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -1.095 <0.001
Income 0.241 <0.001
Occupation 1 0.886 <0.001
Occupation 2 1.028 0.024
Occupation 3 -1.201 0.011
Dependent variable Likelihood that a person intends to buy a car
Model type Logistic regression
Sample size 567.0
R2 nan
Adjusted R2
Pseudo R2 (Cox & Snell) 0.11
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.593 0.032
Income 0.233 <0.001
Occupation 1 0.881 <0.001
Occupation 2 1.044 0.026
Occupation 3 -1.705 0.03
New urban districts -0.469 0.026
No transfer -0.58 0.006
Dependent variable Likelihood that a person intends to buy a car
Model type Logistic regression
Sample size 564.0
R2 nan
Adjusted R2
Pseudo R2 (Cox & Snell) 0.2
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant 1.396 0.005
Income 0.28 <0.001
Occupation 1 1.03 <0.001
Occupation 2 1.013 0.046
Occupation 3 -1.694 0.035
New urban districts -0.627 0.006
No transfer -0.608 0.007
3 (negative externalities) -0.253 0.012
2 (expectations of functionality) -0.649 0.002
1 (affective wellbeing) -0.98 <0.001
4 (fringe benefits) 0.578 <0.001
Dependent variable Likelihood that a person intends to buy a car
Model type Logistic regression
Sample size 553.0
R2 nan
Adjusted R2
Pseudo R2 (Cox & Snell) 0.22
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant 0.315 0.605
Income 0.296 <0.001
Occupation 1 1.012 <0.001
Occupation 2 0.847 0.097
Occupation 3 -1.684 0.038
New urban districts -0.582 0.012
No transfer -0.685 0.003
3 (negative externalities) -0.275 0.008
2 (expectations of functionality) -0.654 0.002
1 (affective wellbeing) -0.912 <0.001
4 (fringe benefits) 0.567 <0.001
Car-use frequency -0.293 0.005
Source variable Target variable Effect p-value Effect type
Female Car use frequency -0.03 0.35 standardized_direct_effect
Single Car use frequency -0.12 <0.001 standardized_direct_effect
Income Car use frequency 0.12 <0.001 standardized_direct_effect
Homeowner Car use frequency 0.26 <0.001 standardized_direct_effect
Occupation 1 (Entrepreneur) Car use frequency -0.06 0.051 standardized_direct_effect
Occupation 2 (Clerk) Car use frequency -0.08 0.02 standardized_direct_effect
Occupation 3 (Student) Car use frequency -0.03 0.416 standardized_direct_effect
New urban districts Car use frequency -0.04 0.183 standardized_direct_effect
Commute walking Car use frequency -0.05 0.126 standardized_direct_effect
Commute duration Car use frequency -0.03 0.388 standardized_direct_effect
Bus access Car use frequency -0.08 0.007 standardized_direct_effect
Status Car use frequency -0.06 0.06 standardized_direct_effect
3 (negative externalities) Car use frequency -0.1 0.006 standardized_direct_effect
4 (fringe benefits) Car use frequency 0.11 0.034 standardized_direct_effect
5 (effectiveness) Car use frequency -0.11 0.017 standardized_direct_effect
Female Public transport use frequency 0.1 0.008 standardized_direct_effect
Single Public transport use frequency 0.16 <0.001 standardized_direct_effect
Income Public transport use frequency -0.1 0.019 standardized_direct_effect
Homeowner Public transport use frequency -0.12 0.005 standardized_direct_effect
Occupation 1 (Entrepreneur) Public transport use frequency 0.09 0.034 standardized_direct_effect
Occupation 2 (Clerk) Public transport use frequency 0.1 0.017 standardized_direct_effect
Occupation 3 (Student) Public transport use frequency 0.1 0.021 standardized_direct_effect
New urban districts Public transport use frequency -0.11 0.008 standardized_direct_effect
Commute walking Public transport use frequency -0.1 0.019 standardized_direct_effect
Commute duration Public transport use frequency 0.11 0.008 standardized_direct_effect
Bus access Public transport use frequency 0.16 <0.001 standardized_direct_effect
Status Public transport use frequency 0.03 0.572 standardized_direct_effect
3 (negative externalities) Public transport use frequency 0.01 0.795 standardized_direct_effect
4 (fringe benefits) Public transport use frequency -0.05 0.48 standardized_direct_effect
5 (effectiveness) Public transport use frequency 0.06 0.337 standardized_direct_effect
Source variable Target variable Effect p-value Effect type
Car owner Car use frequency 0.54 <0.001 standardized_direct_effect
Female Car use frequency -0.04 0.087 standardized_direct_effect
Single Car use frequency -0.03 0.242 standardized_direct_effect
Income Car use frequency -0.02 0.413 standardized_direct_effect
Homeowner Car use frequency 0.14 <0.001 standardized_direct_effect
Occupation 1 (Entrepreneur) Car use frequency -0.08 0.002 standardized_direct_effect
Occupation 2 (Clerk) Car use frequency -0.05 0.049 standardized_direct_effect
Occupation 3 (Student) Car use frequency -0.03 0.218 standardized_direct_effect
New urban districts Car use frequency -0.05 0.049 standardized_direct_effect
Commute walking Car use frequency -0.04 0.161 standardized_direct_effect
Commute duration Car use frequency -0.02 0.385 standardized_direct_effect
Bus access Car use frequency -0.06 0.014 standardized_direct_effect
Status Car use frequency 0.0 0.994 standardized_direct_effect
3 (negative externalities) Car use frequency -0.05 0.078 standardized_direct_effect
4 (fringe benefits) Car use frequency -0.02 0.648 standardized_direct_effect
5 (effectiveness) Car use frequency -0.01 0.768 standardized_direct_effect
Car owner Public transport use frequency -0.26 <0.001 standardized_direct_effect
Female Public transport use frequency 0.11 0.004 standardized_direct_effect
Single Public transport use frequency 0.12 0.004 standardized_direct_effect
Income Public transport use frequency -0.03 0.415 standardized_direct_effect
Homeowner Public transport use frequency -0.07 0.124 standardized_direct_effect
Occupation 1 (Entrepreneur) Public transport use frequency 0.1 0.017 standardized_direct_effect
Occupation 2 (Clerk) Public transport use frequency 0.09 0.029 standardized_direct_effect
Occupation 3 (Student) Public transport use frequency 0.1 0.015 standardized_direct_effect
New urban districts Public transport use frequency -0.11 0.01 standardized_direct_effect
Commute walking Public transport use frequency -0.11 0.012 standardized_direct_effect
Commute duration Public transport use frequency 0.11 0.008 standardized_direct_effect
Bus access Public transport use frequency 0.15 <0.001 standardized_direct_effect
Status Public transport use frequency -0.01 0.909 standardized_direct_effect
3 (negative externalities) Public transport use frequency -0.01 0.846 standardized_direct_effect
4 (fringe benefits) Public transport use frequency 0.01 0.83 standardized_direct_effect
5 (effectiveness) Public transport use frequency 0.01 0.865 standardized_direct_effect

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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