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

Civil Engineering

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