# Table 1 Running example of an agent’s decision-making for a 18km-journey from Sion to Sierre/Siders. Note that the expected utility is a cost function, i.e. agent prefer lower valueFootnoteData collected from (Google Map; Rome2rio website; Mobility and Transport Microcensus 2017; Swiss Household Energy Demand Survey (SHEDS) 2017)

Level Determinant w EU
1st Evaluation (Price - Swiss franc), Belief = 100% 2 EU(car) = 4 EU(train) = 3 EU(bike) = 0
Evaluation (Duration - hours), Belief = 100% 4 EU(car)≈ 0.3 EU(train)≈ 0.2 EU(bike)≈ 1
Norm (similarity with others) 3 EU(train) = 1 EU(car) = 2 EU(bike) = 3
Role (environmental friendliness) 2 EU(car) = 3 EU(train) = 2 EU(bike) = 1
Self-concept (personal preference) 3 EU(car) = 1 EU(train) = 2 EU(bike) = 3
Emotion (enjoyment) 1 EU(car) = 1 EU(train) = 2 EU(bike) = 3
Frequency (past similar trips - note that lower value means more usage) 3 EU(car) = 0 EU(train) = 0 EU(bike) = 1
2st Consequence (Evaluation + Belief) 4 EU(car) = 4/7*2 + 0.3/1.5*4 ≈ 1.94 EU(train) = 3/7*2 + 0.2/1.5*4 ≈ 1.39 EU(bike) = 0/7*2 + 1/1.5*4 ≈ 2.67
Social factors (Norm + Role + Self-concept) 2 EU(car) = 1/6*3 + 3/6*2 + 1/6*3 = 2 EU(train) = 2/6*3 + 2/6*2 + 2/6*3 ≈ 2.67 EU(bike) = 3/6*3 + 1/6*2 + 3/6*3 = ≈ 3.33
Affects (Emotion) 2 EU(car) = 1/6*1 ≈ 0.17 EU(train) = 2/6*1 ≈ 0.33 EU(bike) = 3/6*1 = 0.5
3rd Intention (Consequence + Social factors + Affect) 4 EU(car) = 1.94/6*4 + 2/8*2 + 0.17/1*2 ≈ 2.13 EU(train) = 1.39/6*4 + 2.67/8*2 + 0.33/1*2 ≈ 2.26 EU(bike) = 2.67/6*4 + 3.33/8*2 + 3/1*2 ≈ 3.28
Habit (Frequency) 3 EU(car) = 0/1*3 = 0 EU(train) = 0/1*3 = 0 EU(bike) = 1/1*3 = 3
Facilitating conditions (lower mean easier to access) 2 EU(car) = 0 EU(train) = 1 (agent is far from train station) EU(bike) = 0
Behaviour output   EU(car) = 2.13/7.67*4 + 0/2*3 + 0/2*2 ≈ 1.11 EU(train) = 2.26/7.67*4 + 0/2*3 + 1/2*2 ≈ 2.18 EU(bike) = 3.28/7.67*4 + 1.5/2*3 + 3/2*2 ≈ 6.96