Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility

Jonas De Vos, Patricia L. Mokhtarian, Tim Schwanen, Veronique Van Acker, Frank Witlox, 2016, in Travel Behavior

doi:DOI 10.1007/s11116-015-9619-9
Location Belgian city of Ghent
Population Other (specify)
Sample size 1720
Factor analysis type exploratory factor analysis, promax rotation
Stepwise regression yes
Removal of insignificant variables no
Reviewed by KB

Abstract

Over the past decades research on travel mode choice has evolved from work that is informed by utility theory, examining the effects of objective determinants, to studies incorporating more subjective variables such as habits and attitudes. Recently, the way people perceive their travel has been analyzed with transportation-oriented scales of subjective well-being, and particularly the satisfaction with travel scale. However, studies analyzing the link between travel mode choice (i.e., decision utility) and travel satisfaction (i.e., experienced utility) are limited. In this paper we will focus on the relation between mode choice and travel satisfaction for leisure trips (with travel-related attitudes and the built environment as explanatory variables) of study participants in urban and suburban neighborhoods in the city of Ghent, Belgium. It is shown that the built environment and travel-related attitudes—both important explanatory variables of travel mode choice—and mode choice itself affect travel satisfaction. Public transit users perceive their travel most negatively, while active travel results in the highest levels of travel satisfaction. Surprisingly, suburban dwellers perceive their travel more positively than urban dwellers, for all travel modes. © 2015, Springer Science+Business Media New York.

Factors

Variable Pattern loading
What does your ideal neighborhood look like: sufficient side walks (Res preference/perception) 0.93
What does your ideal neighborhood look like: sufficient bike lanes (Res preference/perception) 0.78
What does your ideal neighborhood look like: sufficient public transit (Res preference/perception) 0.52
What does your ideal neighborhood look like: shops/amenities nearby (Res preference/perception) 0.39
Variable Pattern loading
What does your ideal neighborhood look like: sufficient privacy (Res preference/perception) 0.82
What does your ideal neighborhood look like: peaceful (Res preference/perception) 0.66
What does your ideal neighborhood look like: big dwellings and gardens (Res preference/perception) 0.52
What does your ideal neighborhood look like: high density (Res preference/perception) -0.27
Variable Pattern loading
What does your ideal neighborhood look like: shops/amenities nearby (Res preference/perception) 0.29
Number of positive aspects linked to car use (Mode-liking) -0.46
What does your ideal neighborhood look like: peaceful (Res preference/perception) -0.27
What does your ideal neighborhood look like: high density (Res preference/perception) 0.47
Do you agree? I like to walk (Mode-liking) 0.31
Variable Pattern loading
Do you agree? I like to travel by bike (Mode-liking) 0.78
Number of positive aspects linked to bicycling (Mode-liking) 0.74
Variable Pattern loading
What does your ideal neighborhood look like: good accessibility by car (Res preference/perception) 0.81
What does your ideal neighborhood look like: sufficient parking places (Res preference/perception) 0.67
Number of positive aspects linked to car use (Mode-liking) 0.54
Do you agree? I like to travel by car (Mode-liking) 0.5
Variable Pattern loading
Number of positive aspects linked to bus and tram use (Mode-liking) 0.74
Number of positive aspects linked to train use (Mode-liking) 0.63
Do you agree? I like to travel by tram or bus (Mode-liking) 0.51
Do you agree? I like to travel by train (Mode-liking) 0.46
Variable Pattern loading
Number of positive aspects linked to walking (Mode-liking) 0.96
Do you agree? I like to walk (Mode-liking) 0.32

Models

Dependent variable Affect
Model type Linear regression
Sample size 883.0
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
Variable Coefficient p-value
Constant -1.07 0.0
Pro car (accessibility) 0.14 0.003
Age 0.01 0.0
Cars per household member Concessionary fares public transit Travel time -0.26 0.019
Travel distance 0.05 0.047
Dependent variable Positive evaluation
Model type Linear regression
Sample size 883.0
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
Variable Coefficient p-value
Constant -1.22 0.0
Neighborhood (urban = 1) -0.17 0.045
Pro car (accessibility) 0.21 0.0
Age 0.01 0.006
Student 0.59 0.024
Concessionary fares public transit 0.29 0.047
Travel time -0.11 0.011
Travel distance 0.15 0.007
Dependent variable Affect
Model type Linear regression
Sample size 165.0
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
Variable Coefficient p-value
Constant -1.18 0.0
Pro bicycling 0.22 0.012
Prefer urban built environment 0.26 0.026
Age 0.01 0.017
Driver’s license 0.23 0.039
Concessionary fares public transit 0.85 0.004
Dependent variable Positive evaluation
Model type Linear regression
Sample size 165.0
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
Variable Coefficient p-value
Constant -0.42 0.173
Neighborhood (urban = 1) -0.36 0.021
Pro bicycling 0.24 0.001
Pro public transit 0.3 0.0
Age 0.02 0.0
Travel Time -0.08 0.018
Dependent variable Affect
Model type Linear regression
Sample size 319.0
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
Variable Coefficient p-value
Constant -1.21 0.0
Pro public transit 0.22 0.001
Age 0.01 0.005
Household income 0.12 0.001
Driver’s license 0.49 0.012
Dependent variable Positive evaluation
Model type Linear regression
Sample size 319.0
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
Variable Coefficient p-value
Constant -0.73 0.001
Pro bicycling 0.2 0.002
Pro walking 0.16 0.02
Age 0.01 0.004
Driver’s license 0.22 0.008
Dependent variable Affect
Model type Linear regression
Sample size 337.0
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
Variable Coefficient p-value
Constant -0.92 0.0
Pro car (accessibility) 0.28 0.0
Pro bicycling 0.53 0.0
Prefer urban built environment 0.28 0.001
Age 0.02 0.0
Dependent variable Positive evaluation
Model type Linear regression
Sample size 337.0
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
Variable Coefficient p-value
Constant -0.52 0.0
Pro car (accessibility) 0.22 0.002
Pro bicycling 0.62 0.0
Prefer urban built environment 0.18 0.028
Gender (female = 1) 0.23 0.036
Household car possession 0.19 0.026

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.

sha256:a08d9e369743bf7e6d1c40d27347318209b40a7fb1543813fdcf31b898918815