Recurrent Neural Networks And Crisp-Dm In Traffic Accident Analysis And Prediction In Bucaramanga, Colombia.
DOI:
https://doi.org/10.61799/2216-0388.1716Keywords:
CRISP-DM, prediction, Recurrent Neural Networks (RNN), traffic accidents.Abstract
The work aimed to implement recurrent neural networks (RNN) using the CRISP-DM methodology to analyze and predict the severity, monthly frequency, and daily number of people involved in traffic accidents in Bucaramanga, Colombia. Data collected from January 2012 to September 2023 were used. The CRISP-DM methodology guided all phases of the project, from business and data understanding to modeling and evaluation. RNN models, including Many-to-One and LSTM (Long Short-Term Memory), demonstrated high accuracy in classifying accident severity and moderate accuracy in regressing the number of accidents and people involved. The results provided a valuable tool for traffic authorities in improving road safety. These findings highlight the utility of RNNs and the CRISP-DM methodology in analyzing and predicting traffic accidents, offering a solid basis for informed decisions in road safety management.
Downloads
References
[1] J. Cabrerizo, Manual para la investigacion y reconstrucción de las causas de accidentes de tráfico. Wolters Kluwer Espana, 2016. [Online]. Available: https://elibro.net/es/lc/biblioupc/titulos/55965
[2] M. Valencia-Cárdenas, J. A. Restrepo-Morales, and F. J. Día-Serna, “Big Data Analytics in the Agribusiness Supply Chain Management”, AiBi Revista de Investigación, Administración e Ingeniería, vol. 9, no. 3, pp. 32–42, Sep. 2021, doi: 10.15649/2346030X.2583 DOI: https://doi.org/10.15649/2346030X.2583
[3] J. Quintero, L. Orjuela, J. Gordillo y A. Sánchez-Quiñones, “Análisis de implementación del Big Data en empresas y en profesionales de Contaduría Pública en Colombia. Revista Temario Científico, 2 (1), pp.50-60, 2022. Doi: https://doi.org/10.47212/Alinin.1.2.5 DOI: https://doi.org/10.47212/rtcAlinin.1.2.5
[4] F. Alhaek, W. Liang, T. M. Rajeh, M. H. Javed, and T. Li, “Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach,” Knowl Based Syst, vol. 286, p. 111406, 2024, doi: https://doi.org/10.1016/j.knosys.2024.111406. DOI: https://doi.org/10.1016/j.knosys.2024.111406
[5] J. Casas Roma, T. Lozano Bagén, and A. Bosch Rué, Deep Learning : Principios y Fundamentos. Barcelona, SPAIN: Editorial UOC, 2020. [Online]. Available: http://ebookcentral.proquest.com/lib/universidadviu/detail.action?docID=7025971
[6] J. A. Castilo Romero, Big data. IFCT128PO. IC Editorial, 2019. [Online]. Available: https://elibro.net/es/lc/biblioupc/titulos/124254
[7] X. Yin, G. Wu, J. Wei, Y. Shen, H. Qi, and B. Yin, “Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4927–4943, Jun. 2022, doi:10.1109/tits.2021.3054840 DOI: https://doi.org/10.1109/TITS.2021.3054840
[8] P. D. Smith, Hands-On Artificial Intelligence for Beginners : An Introduction to AI Concepts, Algorithms, and Their Implementation. Birmingham, United Kingdom: Packt Publishing, Limited, 2018. [Online]. Available: http://ebookcentral.proquest.com/lib/universidadviu/detail.action?docID=5607070
[9] G. Bonaccorso, A. Fandango & R. Shanmugamani, Python: Advanced Guide to Artificial Intelligence : Expert Machine Learning Systems and Intelligent Agents Using Python. Packt Publishing, Limited. http://ebookcentral.proquest.com/lib/universidadviu/detail.action?docID=5626921
[10] J. Casas Roma, T. Lozano Bagén, and A. Bosch Rué, Deep Learning: Principios y Fundamentos. Barcelona, SPAIN: Editorial UOC, 2020. [Online]. Available: http://ebookcentral.proquest.com/lib/universidadviu/detail.action?docID=7025971
[11] T. Zhang and Y. Zheng, Introduction to Machine Learning. Springer, 2020. doi: 10.1007/978-3-030-41804-7.
[12] Md. E. Shaik, Md. M. Islam, and Q. S. Hossain, “A review on neural network techniques for the prediction of road traffic accident severity,” Asian Transport Studies, vol. 7, p. 100040, 2021, doi: https://doi.org/10.1016/j.eastsj.2021.100040 DOI: https://doi.org/10.1016/j.eastsj.2021.100040
[13] Y. Zhang and Y. Sung, “Traffic Accident Detection Using Background Subtraction and CNN Encoder–Transformer Decoder in Video Frames,” Mathematics, vol. 11, no. 13, p. 2884, 2023, doi: https://doi.org/10.3390/math11132884 DOI: https://doi.org/10.3390/math11132884
[14] P. D. F, “An Overview of Different Deep Learning Techniques Used in Road Accident Detection,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 11, 2023, doi: https://doi.org/10.14569/IJACSA.2023.0141144 DOI: https://doi.org/10.14569/IJACSA.2023.0141144
[15] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int J Inf Manage, vol. 35, no. 2, pp. 137–144, 2015, doi:10.1016/j.ijinfomgt.2014.10.007 DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
[16] D.C. Rojas Nieves, Y.V. Chirinos Araque, N. Barbera, M.J. Nieves Álvarez “Empleados tóxicos hasta donde son una amenaza para las organizaciones”, Mundo Fesc, vol 13, no. 27, pp. 325-340, 2023. https://doi.org/10.61799/2216-0388.1490 DOI: https://doi.org/10.61799/2216-0388.1490
[17] M. Vergel-Ortega, Z. C. Nieto-Sánchez, C. S. Gómez-Vergel, “Predictores de innovación en programas de ingeniería y postgrado utilizando estrategias basadas en plataformas digitales,” Revista. UIS Ingeniería, vol. 20, no. 1, pp. 213-222, 2021, doi: 10.18273/revuin.v20n1-2021018 DOI: https://doi.org/10.18273/revuin.v20n1-2021018
[18] W. Y. Ayele, “Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, 2020, doi: https://doi.org/10.14569/IJACSA.2020.0110603 DOI: https://doi.org/10.14569/IJACSA.2020.0110603
[19] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol: O’Reilly Media, 2019.
[20] A. So, T. V. Joseph, R. Thas John, A. Worsley, and S. Asare, The Data Science Workshop : A New, Interactive Approach to Learning Data Science. Birmingham, UNITED KINGDOM: Packt Publishing, Limited, 2020. [Online]. Available: http://ebookcentral.proquest.com/lib/universidadviu/detail.action?docID=6033288
[21] H. Wiemer, L. Drowatzky, and S. Ihlenfeldt, “Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model,” Applied Sciences, vol. 9, no. 12, 2019, doi: https://doi.org/10.3390/app9122407 DOI: https://doi.org/10.3390/app9122407
[22] D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, Academic Press, 2018, pp. 542–545. doi:10.1016/B978-0-12-809633-8.20483-8 DOI: https://doi.org/10.1016/B978-0-12-809633-8.20349-X
[23] X. Zhang, C. Zhong, J. Zhang, T. Wang, and W. W. Y. Ng, “Robust recurrent neural networks for time series forecasting,” Neurocomputing, vol. 526, pp. 143–157, 2023, doi: https://doi.org/10.1016/j.neucom.2023.01.037 DOI: https://doi.org/10.1016/j.neucom.2023.01.037
[24] O. Colliot, Machine Learning for Brain Disorders. in Neuromethods, Vol. 197. New York, NY: Humana, 2023. [Online]. Available: https://doi.org/10.1007/978-1-0716-3195-9 DOI: https://doi.org/10.1007/978-1-0716-3195-9
[25] V. Plotnikova, M. Dumas, and F. P. Milani, “Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements,” Data Knowl Eng, vol. 139, p. 102013, 2022, doi: https://doi.org/10.1016/j.datak.2022.102013 DOI: https://doi.org/10.1016/j.datak.2022.102013
Published
Issue
Section
License
Copyright (c) 2025 Mundo FESC Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
