A bibliometric analysis and benchmark of machine learning and automl in crash severity prediction: the case study of three colombian cities

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dc.contributor.authorAngarita Zapata, Juan S.
dc.contributor.authorMaestre Gongora, Gina
dc.contributor.authorFajardo Calderín, Jenny
dc.date.accessioned2025-09-12T15:10:25Z
dc.date.available2025-09-12T15:10:25Z
dc.date.issued2021-12-16
dc.date.updated2025-09-12T15:10:25Z
dc.description.abstractTraffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.en
dc.description.sponsorshipThis work has been funded by the European Union’s Horizon 2020 Research and Innovation Program under Grant 955273. This work has also been funded by the Spanish Ministry of Science and Innovation through research project PID2019-109393RA-I00.en
dc.identifier.citationAngarita-Zapata, J. S., Maestre-Gongora, G., & Calderín, J. F. (2021). A bibliometric analysis and benchmark of machine learning and automl in crash severity prediction: the case study of three colombian cities. Sensors, 21(24). https://doi.org/10.3390/S21248401
dc.identifier.doi10.3390/S21248401
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3614
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.subject.otherAutomated machine learning
dc.subject.otherCrash severity prediction
dc.subject.otherIntelligent transportation systems
dc.subject.otherInternet of Things
dc.subject.otherMachine learning
dc.subject.otherSupervised learning
dc.titleA bibliometric analysis and benchmark of machine learning and automl in crash severity prediction: the case study of three colombian citiesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue24
oaire.citation.titleSensors
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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