I am the Greatest Driver in the World!: -Does self-awareness of driving ability affect traffic safety behaviour?
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
This simulator study aims to investigate if there is a relationship between self-awareness of driving ability and traffic safety behaviour. Self-awareness in this study is accurate self-evaluation of one’s abilities. By letting 97 participants (55-75 years old) drive the simulator and answering the Driver Skill Inventory (DSI; Warner et al., 2013) as well as the Multidimensional locus of control (T-loc; Özkan & Lajunen, 2005). A measure of self-awareness was computed using the residuals from regression line. Furthermore, this measure could show if a participant over-estimated or under-estimated their ability. Four self-awareness measures were made. The self-awareness measures were compared to traffic safety behaviour. Three different traffic safety measures were computed using specific events in the simulator scenario. The self-awareness measures were grouped into three groups; under-estimators, good self-awareness and over-estimators. These groups were then compared to each other with respect to traffic safety. A multivariate ANOVA was made to test for differences between the self-awareness groups but no significant main difference was found. The results showed no difference in traffic safety behaviour given the different levels of self-awareness. Furthermore, this could be a result of the old age of the sample group as self-awareness may only be relevant in a learning context. The conclusion of the study is that the analysis shows that there is no difference between over-estimators and under-estimators of driving ability, at least not in experienced older drivers.
Place, publisher, year, edition, pages
2015. , p. 43
Keywords [en]
Human factors, Driving Ability, Self-awareness, Traffic Safety Behaviour, Simulator study, Self-assessment, DSI, Traffic locus of control, Over- estimation
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:liu:diva-119620ISRN: LIU-IDA/KOGVET-A--15/008—SEOAI: oai:DiVA.org:liu-119620DiVA, id: diva2:825097
External cooperation
The Swedish National Road and Transport Research Institute
Subject / course
Cognitive science
Presentation
2015-06-02, Grace Hopper, Linköping, 09:19 (Swedish)
Supervisors
Examiners
2015-06-242015-06-232018-01-11Bibliographically approved