| | Users | Attitude |
| | User A | User A is a male from the low middle class with a monthly income of 900€. He owns a bike, a car, and a PT monthly pass. When it comes to his attitude towards the optimization parameters, he assigns a high importance degree to the following factors: travel costs, TTR, CO2 emissions, burnt calories, and specific weather conditions such as cold and rainy. For the parameters of travel time, travel safety, ownership, and other weather conditions such as snowy, humid, and windy, he has a moderate importance degree. Lastly, he assigns a low importance degree to travel comfort, travel quality, and the weather condition of hot weather |
| | User B | User B is a female from the middle class with a monthly income of 1300€. She owns a bike and a PT monthly pass. When considering her attitude towards the optimization parameters, she assigns a high importance degree to specific weather conditions such as hot and humid. For the parameters of travel time, travel costs, TTR, ownership, travel comfort, travel quality, travel safety, and other weather conditions such as rainy and windy, she has a moderate importance degree. Lastly, she assigns a low importance degree to the parameters of CO2 emissions, burnt calories, and weather conditions of cold and snowy weather |
| | User C | User C is a male from the upper middle class with a monthly income of 1750€. He owns a bike, a car, and a PT monthly pass. When considering his attitude towards the optimization parameters, he assigns a high importance degree to travel comfort, travel quality, travel safety, and specific weather conditions such as cold, snowy, and windy. For the parameters of travel time, TTR, ownership, and the weather condition of rainy weather, he has a moderate importance degree. Lastly, he assigns a low importance degree to the parameters of CO2 emissions, burnt calories, travel costs, and weather conditions of hot and humid weather |
|
|
