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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/2213624X
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Supplemental Notes:
- © 2019 Published by Elsevier Ltd on behalf of World Conference on Transport Research Society. Abstract reprinted with permission of Elsevier.
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Authors:
- Moradpour, Samareh
- Long, Suzanna
- Publication Date: 2019-6
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Photos; References; Tables;
- Pagination: pp 178-184
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Serial:
- Case Studies on Transport Policy
- Volume: 7
- Issue Number: 2
- Publisher: Elsevier
- ISSN: 2213-624X
- Serial URL: http://www.sciencedirect.com/science/journal/2213624X
Subject/Index Terms
- TRT Terms: Case studies; Data mining; Multiple criteria decision making; Work zone safety
- Geographic Terms: Missouri
- Subject Areas: Construction; Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01712734
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 29 2019 11:02AM