Using Combined Multi-Criteria Decision-Making and Data Mining Methods for Work Zone Safety: A Case Analysis


Work zone accidents are important concerns for transportation decision-makers. Therefore, knowledge of driving behaviors and traffic patterns are essential for identifying significant risk factors (RF) in work zones. Such knowledge can be difficult obtain in a field study without introducing new risks or driving hazards. This research uses integrated data mining and multi-criteria decision-making (MCDM) methods as part of a simulator-based case study of work zone logistics along a highway in Missouri. The research design incorporates k-mean clustering to cluster driving behavior trends, analytic network process (ANP) to determine weights for criteria that are most likely to impact work zones, and the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the alternatives (clusters). Transportation engineers and decision makers can use results from this case study to identify driving populations most likely to engage in risky driving behaviors within work zones, and to provide guidance on effective work zone management.


Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center


This work was supported by the Missouri Department of Transportation [MoDOT TR201512 ]; the Missouri S&T Engineering Management ; and Systems Engineering Department and the Intelligent Systems Center.

Keywords and Phrases

ANP; Case study; Data mining; k-mean clustering; Multi-criteria decision-making; VIKOR method; Work zone accidents

International Standard Serial Number (ISSN)


Document Type

Article - Journal

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© 2019 World Conference on Transport Research Society, All rights reserved.

Publication Date

01 Jun 2019