Introducing a multi-variate classification method: Risky driving acceptance among different heterogeneous driver sub-cultures |
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| Institution: | 1. Qatar University – Qatar Transportation and Traffic Safety Center, College of Engineering, P.O. Box 2713, Doha, Qatar;2. UHasselt, School of Transportation Sciences, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium;3. Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan;1. Department of Civil, Structural and Environmental Engineering, Engineering Statistics and Econometrics Application Research Laboratory, University at Buffalo, The State University of New York, 204B Ketter Hall, Buffalo, NY, 14260, United States;2. Transport Research Institute, School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK;3. Department of Civil, Structural and Environmental Engineering, Stephen Still Institute for Sustainable Transportation and Logistics, University at Buffalo, The State University of New York, 241 Ketter Hall, Buffalo, NY, 14260, United States;4. Public Safety & Transportation Group, CUBRC, 4455 Genesee St., Suite 106, Buffalo, NY, 14225, United States;1. Gediz University, Department of Psychology, Izmir, Turkey;2. Izmir University of Economics, Department of Psychology, Izmir, Turkey;3. Norwegian University of Science and Technology, Department of Psychology, Trondheim, Norway;1. Institute of Transport Economics, Gaustadalleen 21, 0349 Oslo, Norway;2. Department of Transportation Planning and Engineering, National Technical University of Athens, Zografou Campus, Iroon Polytechniou 5, GR-15773 Athens, Greece |
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| Abstract: | Introduction: Heterogeneous driving populations with many different origins are likely to have various sub-cultures that comprise of drivers with shared driver characteristics, most likely with dissimilar traffic safety cultures. An innovative methodology in traffic safety research is introduced which is beneficial for large datasets with multiple variables, making it useful for the multi-variate classification of drivers, driving attitudes and/or (risky) driving behaviours. Method: With the application of multidimensional scaling analysis (MDS), this study explores traffic safety culture in the State of Qatar using a questionnaire and investigates the similarity patterns between the questionnaire items, aiming to classify attitudes towards risky driving behaviours into themes. MDS is subsequently applied to classify drivers within a heterogeneous driving sample into sub-cultures with shared driver characteristics and different risky driving attitudes. Results: Results show that acceptance of speeding is highest among the young Arabic students and acceptance of distraction and drivers’ negligence such as phone use and not wearing a seatbelt is highest among male Arab drivers. Acceptance of extreme risk-taking like intoxicated driving and red-light running is highest among South-Asian business drivers. Conclusion: It is important and practical to understand risky behavioural habits among sub-cultures and thereby focussing on groups of drivers instead of individuals, because groups are easier to approach and drivers within sub-cultures are found to influence each other. By indicating which groups of drivers are most likely to perform specific risky driving themes, it is possible to target these groups and effectively emphasise certain subsets of risky driving behaviours during training or traffic safety education. Practical Applications: This study provides guidance for the improvement of driver education and targeted traffic safety awareness campaigns, intending to make changes to attitudes and habits within specific driver sub-cultures with the aim to improve traffic safety on the longer term. |
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| Keywords: | Traffic safety culture Risky driving behaviour Multidimensional scaling Heterogeneous driving population Sub-cultures |
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