2026-04-16T18:56:21Zhttps://repository.ucc.edu.co/server/oai/request

oai:repository.ucc.edu.co:20.500.12494/444032024-10-16T20:54:23Zcom_20.500.12494_11col_20.500.12494_48

Maestre Góngora, Gina Paola Angarita, Juan Sebastián Fajardo Calderin, Jenny 21(24) 2022-03-09T15:37:37Z 2022-03-09T15:37:37Z 2021 14248220 https://doi.org/10.3390/s21248401 https://hdl.handle.net/20.500.12494/44403 Angarita-Zapata, J.S., Maestre-Gongora, G. y Calderín, J. F. A Bibliometric analysis and benchmark of machine learning and autoML in crash severity prediction: The case study of three Colombian cities. Sensors 2021, 21, 8401. https://doi.org/10.3390/s21248401 Los accidentes de tráfico son motivo de preocupación en todo el mundo, ya que son una de las principales causas de muerte a nivel mundial. Una de las políticas diseñadas para hacerles frente es el diseño y despliegue de sistemas de seguridad vial. de seguridad vial. Estos tienen como objetivo predecir los accidentes basándose en los registros históricos, proporcionados por las nuevas tecnologías del Internet de las Cosas (IoT), para mejorar la gestión del flujo de tráfico. (IoT), para mejorar la gestión del flujo de tráfico y promover carreteras más seguras. El aumento de los datos El aumento de la disponibilidad de datos ha ayudado al aprendizaje automático (ML) a abordar la predicción de colisiones y su gravedad. La literatura informa de numerosas contribuciones en relación con artículos de estudio, comparaciones experimentales de varias técnicas y el diseño de nuevos métodos en el punto en que la predicción de la gravedad de los accidentes (CSP) y el ML convergen. A pesar de estos avances, y por lo que sabemos, no existen artículos de investigación exhaustivos artículos de investigación que aborden de forma teórica y práctica el problema de la selección de modelos (MSP) en CSP. Por lo tanto, este artículo presenta un análisis bibliométrico y un punto de referencia experimental de ML y aprendizaje automático (AutoML) como un enfoque adecuado para abordar automáticamente el MSP en CSP. Traffic 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. https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001087002 https://orcid.org/0000-0002-2880-9245 gina.maestre@campusucc.edu.co https://scholar.google.com/citations?user=-EfDLGsAAAAJ&hl=en 22 p. Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Software, Medellín y Envigado Ingeniería de sofware Medellín https://www.mdpi.com/1424-8220/21/24/8401 Sensors Perallos, A.; Hernandez-Jayo, U.; Onieva, E.; García-Zuazola, I.J.Intelligent Transport Systems: Technologies and Applications, 1st ed.;Wiley Publishing: Hoboken, NJ, USA, 2015 Silva, P.B.; Andrade, M.; Ferreira, S. Machine learning applied to road safety modeling: A systematic literature review.J. TrafficTransp. Eng. (Engl. Ed.)2020,7, 775–790 Gutierrez-Osorio, C.; Pedraza, C. Modern data sources and techniques for analysis and forecast of road accidents: A review.J. Traffic Transp. Eng. (Engl. Ed.)2020,7, 432 – 446. ang, J.; Zheng, L.; Han, C.; Yin, W.; Zhang, Y.; Zou, Y.; Huang, H. Statistical and machine-learning methods for clearance timeprediction of road incidents: A methodology review.Anal. Methods Accid. Res.2020,27, 100123 Gajendran, C.; Vk, S.; Sg, S.; Swati, P. Different Methods of Accident Forecast Based on Real Data.J. Civ. Environ. Eng.2015,5.1–5 Hutter, F.; Kotthoff, L.; Vanschoren, J. (Eds.)Automated Machine Learning: Methods, Systems, Challenges; Springer: Berlin/Heidelberg,Germany, 2018 Angarita-Zapata, J.S.; Masegosa, A.D.; Triguero, I. General-Purpose Automated Machine Learning for Transportation: A CaseStudy of Auto-sklearn for Traffic Forecasting. InInformation Processing and Management of Uncertainty in Knowledge-Based Systems;Springer International Publishing: Berlin/Heidelberg, Germany, 2020; pp. 728–744. Angarita-Zapata, J.S.; Masegosa, A.D.; Triguero, I. Evaluating Automated Machine Learning on Supervised Regression TrafficForecasting Problems. InComputational Intelligence in Emerging Technologies for Engineering Applications; Springer InternationalPublishing: Berlin/Heidelberg, Germany, 2020; pp. 187–204. Angarita-Zapata, J.S.; Triguero, I.; Masegosa, A.D. A Preliminary Study on Automatic Algorithm Selection for Short-TermTraffic Forecasting. InIntelligent Distributed Computing XII; Del Ser, J., Osaba, E., Bilbao, M.N., Sanchez-Medina, J.J., Vecchio, M.,Yang, X.S., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 204–214 Angarita-Zapata, J.S.; Maestre-Gongora, G.; Calderín, J.F. A Case Study of AutoML for Supervised Crash Severity Prediction.In Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of theEuropean Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on AggregationOperators (AGOP), Bratislava, Slovakia, 19–24 September 2021; Atlantis Press: Paris, France, 2021; pp. 187–194. Erickson, N.; Mueller, J.; Shirkov, A.; Zhang, H.; Larroy, P.; Li, M.; Smola, A. AutoGluon-Tabular: Robust and Accurate AutoMLfor Structured Data.arXiv2020, arXiv:2003.06505 Feurer, M.; Klein, A.; Eggensperger, K.; Springenberg, J.; Blum, M.; Hutter, F. Efficient and Robust Automated Machine Learning.InAdvances in Neural Information Processing Systems; Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., Eds.;Curran Associates, Inc.: Red Hook, NY, USA, 2015; pp. 2962–2970 Olson, R.S.; Bartley, N.; Urbanowicz, R.J.; Moore, J.H. Evaluation of a Tree-based Pipeline Optimization Tool for AutomatingData Science. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, Denver, CO, USA, 20–24 July 2016;pp. 485–492. Song, H.; Triguero, I.; Özcan, E. A review on the self and dual interactions between machine learning and optimisation.Prog.Artif. Intell.2019,8, 1–23 Zöller, M.A.; Huber, M.F. Survey on Automated Machine Learning.arXiv2019, arXiv:1904.12054 Tang, J.; Liang, J.; Han, C.; Li, Z.; Huang, H. Crash injury severity analysis using a two-layer Stacking framework.Accid. Anal.Prev.2019,122, 226–238 Li, P.; Abdel-Aty, M.; Yuan, J. Real-time crash risk prediction on arterials based on LSTM-CNN.Accid. Anal. Prev.2020,135, 105371 Topuz, K.; Delen, D. A probabilistic Bayesian inference model to investigate injury severity in automobile crashes.Decis. SupportSyst.2021,150, 113557 Gao, L.; Lu, P.; Ren, Y. A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents.Reliab. Eng. Syst. Saf.2021, Yang, Z.; Zhang, W.; Feng, J. Predicting multiple types of traffic accident severity with explanations: A multi-task deep learningframework.Saf. Sci.2022,146, 105522. Yu, B.; Bao, S.; Chen, Y.; LeBlanc, D.J. Effects of an integrated collision warning system on risk compensation behavior: Anexamination under naturalistic driving conditions.Accid. Anal. Prev.2021,163, 106450 Mannering, F.; Bhat, C.R.; Shankar, V.; Abdel-Aty, M. Big data, traditional data and the tradeoffs between prediction and causalityin highway-safety analysis.Anal. Methods Accid. Res.2020,25, 100113 Wahyuni, H.; Vanany, I.; Ciptomulyono, U. Food safety and halal food in the supply chain: Review and bibliometric analysis.J. Ind. Eng. Manag.2019,12, 373 Bhatt, Y.; Ghuman, K.; Dhir, A. Sustainable manufacturing. Bibliometrics and content analysis.J. Clean. Prod.2020,260, 120988 Klavans, R.; Boyack, K.W. Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and TechnicalKnowledge?J. Assoc. Inf. Sci. Technol.2016,68, 984–998 Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview andguidelines.J. Bus. Res.2021,133, 285–296 Feng, S.; Li, Z.; Ci, Y.; Zhang, G. Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers.Accid. Anal. Prev.2016,86, 29–39 Li, Z.; Chen, C.; Ci, Y.; Zhang, G.; Wu, Q.; Liu, C.; Qian, Z.S. Examining driver injury severity in intersection-related crashesusing cluster analysis and hierarchical Bayesian models.Accid. Anal. Prev.2018,120, 139–151 Zhang, J.; Li, Z.; Pu, Z.; Xu, C. Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learningand Statistical Methods.IEEE Access2018,6, 60079–60087 Martinez, C.M.; Heucke, M.; Wang, F.Y.; Gao, B.; Cao, D. Driving Style Recognition for Intelligent Vehicle Control and AdvancedDriver Assistance: A Survey.IEEE Trans. Intell. Transp. Syst.2018,19, 666–676. Desjardins, C.; Chaib-draa, B. Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach.IEEE Trans. Intell.Transp. Syst.2011,12, 1248–126 Zhu, L.; Yu, F.R.; Wang, Y.; Ning, B.; Tang, T. Big Data Analytics in Intelligent Transportation Systems: A Survey.IEEE Trans.Intell. Transp. Syst.2019,20, 383–398 Meiring, G.; Myburgh, H. A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.Sensors2015,15, 30653–30682. Young, W.; Sobhani, A.; Lenné, M.G.; Sarvi, M. Simulation of safety: A review of the state of the art in road safety simulationmodelling.Accid. Anal. Prev.2014,66, 89–103 Koesdwiady, A.; Soua, R.; Karray, F. Improving Traffic Flow Prediction With Weather Information in Connected Cars: A DeepLearning Approach.IEEE Trans. Veh. Technol.2016,65, 9508–9517 Zhang, Z.; He, Q.; Gao, J.; Ni, M. A deep learning approach for detecting traffic accidents from social media data.Transp. Res.Part C Emerg. Technol.2018,86, 580–596 Mondal, A.R.; Bhuiyan, M.A.E.; Yang, F. Advancement of weather-related crash prediction model using nonparametric machinelearning algorithms.SN Appl. Sci.2020,2, 1–11. Labib, M.F.; Rifat, A.S.; Hossain, M.M.; Das, A.K.; Nawrine, F. Road Accident Analysis and Prediction of Accident Severityby Using Machine Learning in Bangladesh. In Proceedings of the 2019 7th International Conference on Smart Computing &Communications (ICSCC), Sarawak, Malaysia, 28–30 June 2019; IEEE: New York, NY, USA, 2019. Assi, K.; Rahman, S.M.; Mansoor, U.; Ratrout, N. Predicting Crash Injury Severity with Machine Learning Algorithm Synergizedwith Clustering Technique: A Promising Protocol.Int. J. Environ. Res. Public Health2020,17, 5497. Ahmadi, A.; Jahangiri, A.; Berardi, V.; Machiani, S.G. Crash severity analysis of rear-end crashes in California using statisticaland machine learning classification methods.J. Transp. Saf. Secur.2018,12, 522–546. Lee, S.L. Assessing the Severity Level of Road Traffic Accidents Based on Machine Learning Techniques.Adv. Sci. Lett.2016,22, 3115–3119. Wang, C.; Liu, L.; Xu, C.; Lv, W. Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic MachineLearning Framework.Int. J. Environ. Res. Public Health2019,16, 334 Wang, C.; Liu, L.; Xu, C.; Lv, W. Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic MachineLearning Framework.Int. J. Environ. Res. Public Health2019,16, 334 Lee, J.; Yoon, T.; Kwon, S.; Lee, J. Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using MachineLearning Algorithms: Seoul City Study.Appl. Sci.2019 Garcia, S.; Fernandez, A.; Luengo, J.; Herrera, F. Advanced nonparametric tests for multiple comparisons in the design ofexperiments in computational intelligence and data mining: Experimental analysis of power.Inf. Sci. Dries, A.; Rückert, U. Adaptive concept drift detection.Stat. Anal. Data Min. Asa Data Sci. J.2009,2, 311–327 Waring, J.; Lindvall, C.; Umeton, R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.Artif. Intell. Med.2020,104, 101822 Gunning, D.; Aha, D. DARPA’s Explainable Artificial Intelligence (XAI) Program.AI Mag.2019,40, 44–58 Barredo Arrieta, A.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; Garcia, S.; Gil-Lopez, S.; Molina, D.;Benjamins, R.; et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges towardresponsible AI.Inf. Fusion2020,58, 82–115 Prediccion de accidentes Aprendizaje supervisado Aprendizaje de Maquina Crash severity prediction Supervised learning Machine learning A bibliometric analysis and benchmark of machine learning and autoML in crash severity prediction: The case study of three Colombian cities Artículo http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Atribución – Sin Derivar info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Publicationopen.access