Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams
Joint Authors
Skibniewski, Miroslaw
Sekuła, Przemysław
Vander Laan, Zachary
Farokhi Sadabadi, Kaveh
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-18
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
This paper proposes a clustering approach to predict the probability of a collision occurring in the proximity of planned road maintenance operations (i.e., work zones).
The proposed method is applied to over 54,000 short-term work zones in the state of Maryland and demonstrates an ability to predict work zone collision probabilities.
One of the key applications of this work is using the predicted probabilities at the operational level to help allocate highway response teams.
To this end, a two-stage stochastic program is used to locate response vehicles on the Maryland highway network in order to minimize expected response times.
American Psychological Association (APA)
Sekuła, Przemysław& Vander Laan, Zachary& Farokhi Sadabadi, Kaveh& Skibniewski, Miroslaw. 2018. Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1181121
Modern Language Association (MLA)
Sekuła, Przemysław…[et al.]. Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams. Journal of Advanced Transportation No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1181121
American Medical Association (AMA)
Sekuła, Przemysław& Vander Laan, Zachary& Farokhi Sadabadi, Kaveh& Skibniewski, Miroslaw. Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1181121
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181121