Exploring multiple motivations on urban residents' travel mode choices: An empirical study from Jiangsu Province in China

Geng, Long, Chen, Yue, Li, Li, 2017, in Sustainability (Switzerland)

doi:10.3390/su9010136
Location Jiangsu Province, China
Population General
Sample size 1244
Factor analysis type exploratory factor analysis, unknown rotation
Stepwise regression no
Removal of insignificant variables yes
Reviewed by LCM

Abstract

People's actions are always accompanied with multiple motives. How to estimate the role of the pro-environment motivation under the interference of other motivations will help us to better interpret human environmental behaviors. On the basis of classical motivation theories and travel mode choice research backgrounds, the concepts of pro-environmental and self-interested motivation were defined. Then based on survey data on 1244 urban residents in the Jiangsu Province in China, the multinomial logistic regression model was constructed to examine the effects of multiple motivations, government measures, and demographic characteristics on residents' travel mode choice behaviors. The result indicates that compared to car use, pro-environmental motivation certainly has a significant and positive role in promoting green travel mode choices (walking, bicycling, and using public transport), but this unstable green behavior is always dominated by self-interested motivations rather than the pro-environmental motivation. In addition, the effects of gender, age, income, vehicle ownership, travel distance, and government instruments show significant differences among travel mode choices. The findings suggest that pro-environmental motivation needs to be stressed and highlighted to ensure sustainable urban transportation. However, policies aimed to only increase the public awareness of environment protection are not enough; tailored policy interventions should be targeted to specific groups having different main motivations. © 2017 by the authors.

Factors

Variable Pattern loading
It is urgent to do something about the pollution caused by the car (Environmentalism) 0.784
I am worried about the destruction of the environment caused by cars (Environmentalism) 0.779
Car use is one of the main environmental problems (Environmentalism) 0.745
When I use the car, exhaust gases have a negative effect on the global climate (Environmentalism) 0.742
I think that my use of PT contributes to the protection of the environment (Environmentalism) 0.688
I am aware that my car use will contribute to the smog and haze (Environmentalism) 0.677
Variable Pattern loading
My everyday use of PT is for the purpose of protecting the environment (Environmentalism) 0.889
When I'm making an everyday trip, I always plan to protect the environment (Environmentalism) 0.847
Though using PT, I am not considering the plan of protecting the environment (reversed) (Environmentalism) 0.753
Variable Pattern loading
Taking PT for everyday trips is something that is part of my routine (Other) 0.839
Taking PT for everyday trips does not require active thought (Other) 0.824
Taking PT for everyday trips is something I do automatically (Other) 0.76
Taking PT for everyday trips is something that I do without thinking about it (Other) 0.715
Taking PT for everyday trips is something that gives me a strange feeling when I don't do it (Other) 0.68
Variable Pattern loading
I plan to protect the environment by using PT for everyday trips (Environmentalism) 0.876
My intention to protect the environment by using PT for everyday trips is very strong (Environmentalism) 0.834
Variable Pattern loading
My autonomy to use PT for everyday trips is large even in challenging circumstances (Convenience) 0.821
Once I choose PT for everyday trips, it would be easy for me to stick to it even in bad weather (Other) 0.813
I can conquer conditions that are not conducive to my use of PT for everyday trips (Convenience) 0.797
Variable Pattern loading
Because of my own values, I feel a responsibility to use PT for everyday trips (Social norms) 0.848
Regardless of what others do, I feel an obligation to use PT for everyday trips (Other) 0.82
The aspect of environmental protection in travel mode choice is solidly anchored in my values (Environmentalism) 0.814
Variable Loading
Cognition (Other) 0.247
Habit (Other) 0.208
Personal norm (Other) 0.16
Goal (Other) 0.149
PBC (Convenience) 0.123
Intention (Other) 0.114

Models

Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (Cox and Snell) 0.03
AIC nan
BIC nan
Log-likelihood at zero -1698.92
Log-likelihood at constants -1621.81
Log-likelihood at convergence -1605.755
Bicycle
Variable Coefficient p-value
Constant 0.25 <0.01
Pro-environmental motivation 0.76 <0.01
PT
Variable Coefficient p-value
Constant 0.15 <0.1
Pro-environmental motivation 0.96 <0.01
Walking
Variable Coefficient p-value
Constant -0.95 <0.01
Pro-environmental motivation 0.69 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (Cox and Snell) 0.45
AIC nan
BIC nan
Log-likelihood at zero -1698.92
Log-likelihood at constants -1629.645
Log-likelihood at convergence -1256.94
Bicycle
Variable Coefficient p-value
Constant 1.06 <0.1
Pro-environmental motivation 0.46 <0.05
Distance -0.2 <0.01
Gender -0.45 <0.1
Household income -0.02 <0.05
Ownership (bicycle) 2.51 <0.01
Ownership (car) -3.41 <0.01
PT
Variable Coefficient p-value
Pro-environmental motivation 0.74 <0.01
Gender -0.58 <0.05
Ownership (car) -3.4 <0.01
Infrastructure 0.14 <0.1
Walking
Variable Coefficient p-value
Constant -1.58 <0.1
Pro-environmental motivation 0.65 <0.05
Distance -0.51 <0.01
Age -0.15 <0.1
Ownership (car) -3.54 <0.01
Advertising 0.43 <0.01
Policy -0.41 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Comfort -0.07 nan
Constant 0.46 <0.01
PT
Variable Coefficient p-value
Comfort -0.02 nan
Constant 0.189 nan
Walking
Variable Coefficient p-value
Comfort -0.02 nan
Constant -0.93 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Convenience 0.01 nan
Constant 0.2 nan
PT
Variable Coefficient p-value
Convenience 0.15 nan
Constant -0.53 <0.05
Walking
Variable Coefficient p-value
Convenience -0.02 nan
Constant -0.91 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Safety -0.03 nan
Constant 0.65 <0.05
PT
Variable Coefficient p-value
Safety -0.09 nan
Constant 0.58 <0.1
Walking
Variable Coefficient p-value
Safety -0.03 nan
Constant -0.87 <0.05
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Economy 0.12 nan
Constant -0.14 nan
PT
Variable Coefficient p-value
Economy 0.03 nan
Constant 0.03 nan
Walking
Variable Coefficient p-value
Economy 0.02 nan
Constant -1.05 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Health 0.09 nan
Constant 0.01 nan
PT
Variable Coefficient p-value
Health 0.11 nan
Constant -0.15 nan
Walking
Variable Coefficient p-value
Health 0.22 nan
Constant -1.56 <0.01
Dependent variable Mode choice
Model type Multinomial logistic regression
Sample size 1244.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Bicycle
Variable Coefficient p-value
Pro-environmental motivation 0.01 nan
Constant 0.25 nan
PT
Variable Coefficient p-value
Pro-environmental motivation -0.21 nan
Constant 0.71 <0.01
Walking
Variable Coefficient p-value
Pro-environmental motivation -0.19 nan
Constant -0.46 <0.05

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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