Road transportation accounts for a lot of CO2 emissions and they are still increasing. Intelligent transport systems can effectively reduce emissions from vehicles, and the European Commission launched a directive in 2010 to speed up the deployment of these systems. User acceptance and demand are important factors for the deployment process and can be measured in different ways. The purpose of this Master's thesis was to study users' awareness, experience, attitude, demand and willingness to pay for advanced driver support systems. The data was collected through a questionnaire administered in five European countries and analysed depending on gender and age. Systems included in the study were: speed alert, emergency braking, eco-driving assistance, real-time traffic information, start-stop assistance and a tyre pressure monitoring system. Awareness of the selected systems varied a little, but in general around 60% of the respondents had read about, heard of or tried the systems. The actual usage was low, 5-19% depending on the system. Respondents who had tried the systems were asked how often they used it, revealing that most systems were not used regularly. The respondents also had to evaluate the perceived usefulness of the systems on different road types (urban environments, highways and rural roads) and the perceived importance of the systems' benefits. All the systems were seen as useful on at least one road type. Depending on the system, different benefits were included, but at least one benefit for every system was viewed to be important. Around 50% of the respondents wanted to have the systems in their next car and around 60% would be willing to pay something for the systems, usually less than 200. Another purpose of this study was to determine the users' acceptance, early adoption and unawareness of the systems. The analyses were done using logistic regression. Variables included in the acceptance, early adoption and unawareness analyses were determined based on the literature review of previous user acceptance studies. For the acceptance analysis statistically significant variables increasing the respondents' acceptance were: buying their next car as new, frequent usage, high perceived usefulness, and benefits of the systems. For early adoption these were household income, vehicle mileage and the price of their next car. For unawareness they were gender, vehicle mileage and price of their next car.
|Place of Publication||Espoo|
|Publication status||Published - 2015|
|MoE publication type||G2 Master's thesis, polytechnic Master's thesis|
- intelligent transport systems
- driver support systems
- user acceptance
- early adopter