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Identifying Differences in Safe Roads and Crash Prone Roads Using Clustering Data Mining

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Engineering Asset Management 2011

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilize in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of this method is to partition the road network into a set of groups (classes) based on common data and characterize the crash types to produce a crash profile for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

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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 [11, 13–15]. Queensland Department of Transport and Main Roads provided the road and crash datasets. 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

  1. Computer Science Discipline, Science and Technology, Queensland University of Technology, Brisbane, QLD, Australia

    D. Emerson & R. Nayak

  2. Road Asset Management Branch, Queensland Government Department of Main Roads, Brisbane, QLD, Australia

    J. Weligamage

Authors
  1. D. Emerson
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  2. R. Nayak
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  3. J. Weligamage
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Corresponding author

Correspondence to D. Emerson .

Editor information

Editors and Affiliations

  1. Dept. Mechanical, Industrial & Nuclear Engineering (MINE), University of Cincinnati College of Engineering, Cincinnati, Ohio, USA

    Jay Lee

  2. University of Michigan Dept. Mechanical Engineering, Ann Arbor, Michigan, USA

    Jun Ni

  3. Missouri University of Technology, Rolla, USA

    Jagnathan Sarangapani

  4. Management (CIEAM), Queensland University of Technology Centre for Integrated Engineering Asset, Brisbane, Queensland, Australia

    Joseph Mathew

Appendix 1

Appendix 1

Table 10.2 Case study cluster details
Full size table

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Emerson, D., Nayak, R., Weligamage, J. (2014). Identifying Differences in Safe Roads and Crash Prone Roads Using Clustering Data Mining. In: Lee, J., Ni, J., Sarangapani, J., Mathew, J. (eds) Engineering Asset Management 2011. Lecture Notes in Mechanical Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-4993-4_10

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  • DOI: https://doi.org/10.1007/978-1-4471-4993-4_10

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4992-7

  • Online ISBN: 978-1-4471-4993-4

  • eBook Packages: EngineeringEngineering (R0)

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