Abstract
Applying Cluster Analysis to Validate a High Risk Young Driver Model: Implications for Tailored Road Safety Intervention
Proceedings of the 2017 Australasian Road Safety Conference
Australasian Road Safety Conference, 2017 (Perth, Australia, 10-Oct-2017–12-Oct-2017)
2017
Abstract
This project explored the feasibility and efficacy of a model that could be used to identify high risk young drivers (HRYD). The present findings pertain to the final stage, which was to assess the predictive ability of the HRYD model. The two-step cluster analysis method was employed with de-identified Police records of 2,973 Sunshine Coast residents aged 17-24 years. The four clusters in the final solution aligned relatively consistently with the HRYD clusters developed during the earlier phases (literature review, focus groups) of this pioneering project. The HRYD model can be used to guide intervention targeting high risk youth behaviour.
Details
- Title
- Applying Cluster Analysis to Validate a High Risk Young Driver Model: Implications for Tailored Road Safety Intervention
- Authors
- Bridie Scott-Parker (Author) - University of the Sunshine Coast - Faculty of Arts, Business and LawLeanne Stokes (Author) - Queensland Department of Transport and Main RoadsStuart Gardner (Author) - Queensland Department of Transport and Main RoadsMegan Cawkwell (Author) - Sunshine Coast CouncilMatthew Wilson (Author) - Queensland Police ServiceShane Panoho (Author) - Queensland Police ServiceSherryn Klump (Author) - Queensland Police Service
- Publication details
- Proceedings of the 2017 Australasian Road Safety Conference; 5
- Conference details
- Australasian Road Safety Conference, 2017 (Perth, Australia, 10-Oct-2017–12-Oct-2017)
- Publisher
- Australasian College of Road Safety
- Date published
- 2017
- Copyright note
- Copyright © 2017 The Author. Reproduced with permission.
- Organisation Unit
- School of Social Sciences - Legacy; University of the Sunshine Coast, Queensland; School of Law and Society; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99451245202621
- Output Type
- Abstract
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