Abstract
According to recent reports, road traffic injuries are the leading cause of death among children and young adults. Various systems and strategies have been designed to reduce accident severity. With the development of data mining tools, the use of big traffic data and machine learning techniques holds potential for implementing effective road safety strategies. Using a dataset collected from Addis Ababa, Ethiopia, our research introduces an innovative severity prediction system integrating a hybrid Feature Selection (FS) approach, named OWABPSO, with machine learning algorithms. The OWABPSO approach combines a One-Way-ANOVA-based filter method with a Binary Particle Swarm Optimization-based wrapper method. Six algorithms, including K-Nearest Neighbors, Random Forest, Decision Tree, Light Gradient Boosting Machine, Artificial Neural Network, and Extreme Gradient Boosting, are proposed for severity prediction. Experimental outcomes of this work demonstrate that, compared to state-of-the-art methods, by combining our FS approach with Decision Tree-based classifiers, we achieved competitive results. Our study presents an effective integration of FS approaches in predicting accident severity levels, thus contributing to advanced road safety strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Road traffic injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries. Accessed 19 Nov 2023
Jamal, A., et al.: Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study. Int. J. Inj. Contr. Saf. Promot. 28(4), 408–427 (2021). https://doi.org/10.1080/17457300.2021.1928233
Kumeda, B., Zhang, F., Zhou, F., Hussain, S., Almasri, A., Assefa, M.: Classification of road traffic accident data using machine learning algorithms. In: 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), Chongqing, China, pp. 682–687. IEEE, June 2019. https://doi.org/10.1109/ICCSN.2019.8905362
Zhou, Z.-H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia: International Joint Conferences on Artificial Intelligence Organization, pp. 3553–3559, August 2017. https://doi.org/10.24963/ijcai.2017/497
Gan, J., Li, L., Zhang, D., Yi, Z., Xiang, Q.: An alternative method for traffic accident severity prediction: using deep forests algorithm. J. Adv. Transp. 2020, 1–13 (2020). https://doi.org/10.1155/2020/1257627
Al Mamlook, R.E., Ali, A., Hasan, R.A., Mohamed Kazim, H.A.: Machine learning to predict the freeway traffic accidents-based driving simulation. In: 2019 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, pp. 630–634. IEEE, July 2019. https://doi.org/10.1109/NAECON46414.2019.9058268
Labib, Md.F., Rifat, A.S., Hossain, Md.M., Das, A.K., Nawrine, F.: Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In: 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, pp. 1–5. IEEE, June 2019. https://doi.org/10.1109/ICSCC.2019.8843640
Chen, H., Zhao, Y., Ma, X.: Critical factors analysis of severe traffic accidents based on Bayesian network in China. J. Adv. Transp. 2020, e8878265 (2020). https://doi.org/10.1155/2020/8878265
Li, K., Xu, H., Liu, X.: Analysis and visualization of accidents severity based on LightGBM-TPE. Chaos Solitons Fractals 157, 111987 (2022). https://doi.org/10.1016/j.chaos.2022.111987
Bedane, T.T.: Road traffic accident dataset of addis ababa city [Object], 02 November 2020. https://doi.org/10.17632/XYTV86278F.1
Hamim, M., El Moudden, I., Pant, M.D., Moutachaouik, H., Hain, M.: A hybrid gene selection strategy based on fisher and ant colony optimization algorithm for breast cancer classification. Int. J. Online Eng. 17(02), 148 (2021). https://doi.org/10.3991/ijoe.v17i02.19889
Hamim, M., El Mouden, I., Ouzir, M., Moutachaouik, H., Hain, M.: A novel dimensionality reduction approach to improve microarray data classification. IIUMEJ 22(1), 1–22 (2021). https://doi.org/10.31436/iiumej.v22i1.1447
El Moudden, I., Ouzir, M., ElBernoussi, S.: Feature selection and extraction for class prediction in dysphonia measures analysis: a case study on Parkinson’s disease speech rehabilitation. Technol. Health Care off. J. Eur. Soc. Eng. Med. 25, 1–16 (2017). https://doi.org/10.3233/THC-170824
Moutachaouik, H., El Moudden, I.: Mining prostate cancer behavior using parsimonious factors and shrinkage methods (2018)
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., Lin, C.: Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1), 59–70 (2012)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)
Guyon, I., Elisseeff, A.: An introduction to feature extraction. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction. SFSC, vol. 207, pp. 1–25. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-35488-8_1
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995. https://doi.org/10.1109/ICNN.1995.488968
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Computational Cybernetics and Simulation 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108, October 1997. https://doi.org/10.1109/ICSMC.1997.637339
Wei, J., et al.: A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection. Appl. Soft Comput. 58, 176–192 (2017). https://doi.org/10.1016/j.asoc.2017.04.061
BinSaeedan, W., Alramlawi, S.: CS-BPSO: hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis. Knowl. Based Syst. 227, 107224 (2021). https://doi.org/10.1016/j.knosys.2021.107224
Kushwaha, N., Pant, M.: Link based BPSO for feature selection in big data text clustering. Future Gener. Comput. Syst. 82, 190–199 (2018). https://doi.org/10.1016/j.future.2017.12.005
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory 13(1), 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964
Hamim, M., El Moudden, I., Moutachaouik, H., Hain, M.: Decision tree model based gene selection and classification for breast cancer risk prediction. In: Hamlich, M., Bellatreche, L., Mondal, A., Ordonez, C. (eds.) SADASC 2020. CCIS, vol. 1207, pp. 165–177. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45183-7_12
Breiman, L.: Random Forests–Random Features, p. 29 (1999)
Cachim, P.: Using artificial neural networks for calculation of temperatures in timber under fire loading. Constr. Build. Mater. 25, 4175–4180 (2011). https://doi.org/10.1016/j.conbuildmat.2011.04.054
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2017). Accessed 14 Dec 2023. https://proceedings.neurips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html
Ibrahem Ahmed Osman, A., Najah Ahmed, A., Chow, M.F., Feng Huang, Y., El-Shafie, A.: Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Eng. J. 12(2), 1545–1556 (2021). https://doi.org/10.1016/j.asej.2020.11.011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hamim, M., Enaanai, A., Jadli, A., Moutachaouik, H., EL Moudden, I. (2024). A Binary Particle Swarm Optimization Based Hybrid Feature Selection Method for Accident Severity Prediction. In: Hamlich, M., Dornaika, F., Ordonez, C., Bellatreche, L., Moutachaouik, H. (eds) Smart Applications and Data Analysis. SADASC 2024. Communications in Computer and Information Science, vol 2167. Springer, Cham. https://doi.org/10.1007/978-3-031-77040-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-77040-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-77039-5
Online ISBN: 978-3-031-77040-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)