Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers

Author:

Madushani J.P.S. Shashiprabha,Sandamal R.M. Kelum,Meddage D.P.P.,Pasindu H.R.ORCID,Gomes P.I. AyanthaORCID

Funder

Rural Development Administration

Publisher

Elsevier BV

Subject

Mechanical Engineering,Safety, Risk, Reliability and Quality,Aerospace Engineering,Automotive Engineering,Civil and Structural Engineering

Reference70 articles.

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2. Analysis of roadway and environmental factors affecting traffic crash severities;Wang,2016

3. Guido, G., Haghshenas, S., Vitale, A., Astarita, V., Park, Y., & Geem, Z.W. (2022). Evaluation of contributing factors affecting number of vehicles involved in crashes using machine learning techniques in rural roads of Cosenza, Italy. Safety.

4. Non-crossing rail-trespassing crashes in the past decade: a spatial approach to analysis of injury severity;Wang;Saf. Res.,2016

5. Environmental and traffic effects on incident frequency occurred on urban expressways;Zhang,2013

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