Title

Predicting Reduced Visibility Related Crashes On Freeways Using Real-Time Traffic Flow Data

Keywords

Freeways; Matched case-control logistic regression; Random Forests; Real-time crash prediction; Reduced visibility

Abstract

Objectives: The main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions. Method: Random Forests and matched case-control logistic regression models were estimated. Results: The findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes. Impact on Industry: Using time slices 5-15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time. © 2013 Elsevier Ltd.

Publication Date

6-1-2013

Publication Title

Journal of Safety Research

Volume

45

Number of Pages

29-36

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jsr.2012.12.004

Socpus ID

84878099291 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84878099291

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