Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3434
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dc.contributor.authorShashiprabha, M.J.P.S-
dc.contributor.authorKelum, S.R.M-
dc.contributor.authorMeddage, D.P.P-
dc.contributor.authorPasindu, H.R-
dc.contributor.authorGomes, P.I.A-
dc.date.accessioned2023-07-27T08:16:35Z-
dc.date.available2023-07-27T08:16:35Z-
dc.date.issued2023-07-09-
dc.identifier.issn2666691X-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3434-
dc.description.abstractThe number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models. © 2023en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesTransportation Engineering;Volume 13-
dc.subjectExplainable machine learningen_US
dc.subjectExpresswaysen_US
dc.subjectLogistic regressionen_US
dc.subjectMachine learningen_US
dc.subjectTraffic crash severityen_US
dc.titleEvaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiersen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.treng.2023.100190en_US
Appears in Collections:Department of Civil Engineering

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