Abstract
Road asset managers are seeking analysis of the whole road network to supplement statistical analyses of small subsets of homogeneous roadway. This study outlines the use of data mining capable of analyzing the wide range of situations found on the network, with a focus on the role of skid resistance in the cause of crashes. Results from the analyses show that on non-crash-prone roads with low crash rates, skid resistance contributes only in a minor way, whereas on high-crash roadways, skid resistance often contributes significantly in the calculation of the crash rate. The results provide evidence supporting a causal relationship between skid resistance and crashes and highlight the importance of the role of skid resistance in decision making in road asset management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization (2003) Global health: today’s challenges. World Health Organization. http://www.who.int/whr/2003/en/Chapter1.pdf. Accessed 12 Nov 2011
QDTMR (2005) Road planning and design manual, design philosophy, Chap. 2. Queensland Department of Transport and MainRoads. http://www.tmr.qld.gov.au/Business-and-industry/Technical-standards-and-publications/Road-planning-and-design-manual.aspx. Accessed 11 Feb 2011
Norsemeter (2009) Primer on modern runway surface friction measurement. Oscarsgate 27, Oslo, Norway: Norsmeter Friction. http://www.norsemeter.no/Downloads/Files/Runway%20Primer.pdf. Accessed 15 June 2009
Venz P (2005) Skid resistance survey Bruce highway (Middle Creek to Christies Rd), Queensland, Ed. Department of Transport and Main Roads Queensland, Brisbane
ARRB Group (2011) Tools for improving road safety. ARRB Group. Accessed from http://www.arrb.com.au/Safe-Systems/Tools-for-improving-road-safety.aspx, July 2011
Cairney P (2008) Road surface characteristics and crash occurrence: Austroads skid resistance literature review. Austroads Aust. Accessed from http://nla.gov.au/nla.cat-vn4406507. Accessed 12 June 2010
Piyatrapoomi N, Weligamage J (2008) Probability-based method for analysing the relationship between skid resistance and road. In: Proceedings of the laboratory of transportation engineering and infrastructure
Nayak R, Emerson D (2010) Using data mining on road asset management data in analysing road crashes. In: Proceeding of the 16th annual TMR engineering & technology forum, Brisbane, Australia, 20 July 2010
Nayak R, Emerson D, Weligamage J, Piyatrapoomi N (2011) Road crash proneness prediction using data mining. In: Proceedings of the 14th international conference on extending database technology, Uppsala, Sweden, 22–24 March 2011
Emerson D, Nayak R, Weligamage J (2011) Identifying differences in safe roads and crash prone roads using clustering data mining. In: Proceedings of the World Congress on Engineering Asset Management, Cincinnati, Ohio, USA, 3–5 Oct 2011
Emerson D, Nayak R, Weligamage J (2011) Using data mining to predict road crash count with a focus on skid resistance values. In: Proceeding of the 3rd international road surface friction conference, Gold Coast, Queensland, Australia, 15–18 May 2011
Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
Quinlan JR (1992) Learning with continuous classes. AI 92
Acknowledgments
This paper was developed within the CRC for Infrastructure and Engineering Asset Management (CIEAM), established, and supported under the Australian Government’s Cooperative Research Centres Programme. The authors gratefully acknowledge the financial support provided by CIEAM. This CIEAM study is a component of a project involving QUT and QDTMR in the development of a skid resistance decision support system for road asset management. Queensland Department of Transport and Main Roads provided the road and crash data sets. Data mining operations and presentations were performed in the WEKA and SAS platforms, and substantial computer processing was performed on the QUT High-Performance Computing Facility (HPC). The views presented in this paper are of the authors and not necessarily the views of the organizations.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Emerson, D., Nayak, R., Weligamage, J.Z. (2015). Data Mining the Relationship Between Road Crash and Skid Resistance. In: Lee, W., Choi, B., Ma, L., Mathew, J. (eds) Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06966-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-06966-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02461-5
Online ISBN: 978-3-319-06966-1
eBook Packages: EngineeringEngineering (R0)