Fig 1.
General scheme of attitude-related models like TPB and TAM (own presentation, based on various sources).
Fig 2.
Hybrid choice models (adapted from [22]).
Fig 3.
Standard model of decision making (adapted from [29]).
Fig 4.
The Extended Model of Mobility Behaviour (xMooBe).
Table 1.
Participation of the three function groups (N = 10,782, source: [41]).
Table 2.
Modal split of UA-Ruhr members based on main modes of transport (source: [41]).
Table 3.
Preferences related to six goals and average, mode-specific probabilities (N = 10,782, source: [41]).
Table 4.
Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40).
Fig 5.
Actually used and highest rated modes of transport (own illustration).
Fig 6.
Non-utilization of transportation modes despite highest rating; grey = non-utilization of the bike, blue = non-utilization of the car, green = non-utilization of public transport; Bar not labelled = low correlation (<.100) or only weakly significant (<.05); missing bars = no effect (own illustration).
Table 5.
Binary logistic regression model with the dependent variable “car as main mode of transport” (dummy: 1 = car, 0 = other); values for the different models represent the regression coefficients B; odds ratio Exp (B) in parentheses (own illustration).
Table 6.
Probability of car use for two fictitious persons (values in brackets: variable cannot be changed).
Table 7.
Binary logistic regression model with the dependent variable “public transport as main mode of transport” (dummy: 1 = public transport, 0 = other); Variable values for the different models represent the regression coefficients B; odds ratio Exp (B) in parentheses (own illustration).
Table 8.
Probability of using public transport by two fictitious persons (values in brackets: variable cannot be changed).
Table 9.
Binary logistic regression model with the dependent variable “bicycle as main mode of transport” (dummy: 1 = bike, 0 = other); Variable values for the different models represent the regression coefficients B; odds ratio Exp (B) in parentheses (own illustration).
Table 10.
Probability of bicycle use by two fictitious persons (values in brackets: variable cannot be changed).