Literature DB >> 23708473

Predicting reduced visibility related crashes on freeways using real-time traffic flow data.

Hany M Hassan1, Mohamed A Abdel-Aty.   

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.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 23708473     DOI: 10.1016/j.jsr.2012.12.004

Source DB:  PubMed          Journal:  J Safety Res        ISSN: 0022-4375


  1 in total

1.  Examining the effect of adverse weather on road transportation using weather and traffic sensors.

Authors:  Yichuan Peng; Yuming Jiang; Jian Lu; Yajie Zou
Journal:  PLoS One       Date:  2018-10-16       Impact factor: 3.240

  1 in total

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