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

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dc.contributor.author Shashiprabha Madushani, JPS
dc.contributor.author Sandamal, RMK
dc.contributor.author Meddage, DPP
dc.contributor.author Pasindu, HR
dc.contributor.author Gomes, PIA
dc.date.accessioned 2023-12-01T09:11:31Z
dc.date.available 2023-12-01T09:11:31Z
dc.date.issued 2023
dc.identifier.citation Madushani, J. P. S. S., Sandamal, R. M. K., Meddage, D. P. P., Pasindu, H. R., & Gomes, P. I. A. (2023). Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers. Transportation Engineering, 13, 100190. https://doi.org/10.1016/j.treng.2023.100190 en_US
dc.identifier.issn 2666-691X en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21884
dc.description.abstract The 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. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Explainable machine learning en_US
dc.subject Machine learning en_US
dc.subject Traffic crash severity en_US
dc.subject Expressways en_US
dc.subject Logistic regression en_US
dc.title Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers en_US
dc.type Article-Full-text en_US
dc.identifier.year 2023 en_US
dc.identifier.journal Transportation Engineering en_US
dc.identifier.volume 13 en_US
dc.identifier.database Science Direct en_US
dc.identifier.pgnos 100190(1-14) en_US
dc.identifier.doi https://doi.org/10.1016/j.treng.2023.100190 en_US


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