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.
S. Moradpour and S. Long, "Using Combined Multi-Criteria Decision-Making and Data Mining Methods for Work Zone Safety: A Case Analysis," Case Studies on Transport Policy, vol. 7, no. 2, pp. 178-184, Elsevier Ltd, Jun 2019.
The definitive version is available at https://doi.org/10.1016/j.cstp.2019.04.008
Engineering Management and Systems Engineering
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)
Article - Journal
© 2019 World Conference on Transport Research Society, All rights reserved.
01 Jun 2019