Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Journal: Traffic injury prevention

PMID: 39325686

Abstract

OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes.

Authors

Maryam Hasanpour

Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada.
Bhagwant Persaud

Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada.
Robert Mansell

Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada.
Craig Milligan

Miovision, Winnipeg, Manitoba, Canada.

Keywords

Accidents, Traffic Environment Design Humans Machine Learning Models, Statistical Neural Networks, Computer Regression Analysis Safety Video Recording Wounds and Injuries

External Resources

View on PubMed Access via DOI PubMed (39325686)

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Abstract

Authors

Keywords

External Resources

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