Abstract
Road crash is one of the major burning issues for Bangladesh. There are several factors that are responsible for occurring road crashes. If we can understand the causes and predict the severity level of a particular type of accident upfront, we can take necessary steps in the proper time to lessen the damages. In this study, we have built some predictive models of different homogeneous road crash groups of Bangladesh using machine learning methods that can predict that particular road crash severity level based on the environmental factors and road conditions. We have applied Agglomerative Hierarchical Clustering to find different clusters of road crashes and then applied Random Forest technique to extract the significant predictors of each cluster and then applied C5.0 to build predictive models of each cluster. Finally we have discussed the patterns of fatal and non-fatal accidents of Bangladesh through rule generation technique.
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References
Abirami, K.: “Academia’’. https://academia.edu/11542944/MAN_MADE_DISASTER
“The World Bank’’. https://data.worldbank.org/indicator/IS.VEH.NVEH.P
“Association for Safe International Road Travel’’. https://www.asirt.org/safe-travel/road-safety-facts/
“JBCPS’’. https://www.banglajol.info
Hoque, M., Anowar, S., Raihan, M.: Towards sustainable road safety in Bangladesh. In: International Conference on Sustainable Transport for Developing Countries: Concerns, Issues and Options, Dhaka (2008)
Sadeek, S., Anik, Md.: Effect of Road Infrastructures on Casualty Occurrence in Bangladesh. (2018)
Anowar, S., Yasmin, S., Tay, R.: Factors influencing the severity of intersection crashes in bangladesh. Asian Transp. Stud. 3, 1–12 (2014)
Raihan, M., Hossain, M., Hasan, T.: Data mining in road crash analysis: the context of developing countries. Int. J. Inj. Control Saf. Promot. 25(1), 1–12 (2017)
Li, L., Shrestha, S., Hu, G.: Analysis of road traffic fatal accidents using data mining techniques, pp. 363–370. (2017). https://doi.org/10.1109/sera.2017.7965753
Al-Radaideh, Q.A., Daoud, E.J.: Data mining methods for traffic accident severity prediction (2018)
Bahiru, T.K., Singh, D.K., Tessfaw, E.A.: Comparative study on data mining classification algorithms for predicting road traffic accident severity. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT 2018), Coimbatore, pp. 1655–1660 (2018)
Ramya, S., Reshma, SK., Manogna, V., Saroja, Y., Gandhi, G.: Accident severity prediction using data mining methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 528–536 (2019). https://doi.org/10.32628/cseit195293
Sohn, S.Y., Lee, S.H.: Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea. Saf. Sci. 41, 1–14 (2003). https://doi.org/10.1016/S0925-7535(01)00032-7
Thomas, A., Jacobs, G., Saxton, B., Gururaj, G., Rahman, F.: The Involvement and Impact of Road Crashes on the Poor. TRL Limited, UK (2004)
Alam, M., Karim, D., Hoque, M., Islam, Q., Alam, M.: Initiatives regarding road accident database in Bangladesh. In: International Conference on Road Safety in Developing Countries, Dhaka (2006)
Accident Research Institute(ARI). http://ari.buet.ac.bd/
A. R. Institute: Road Safety Training Course For partitioners
Clustering Validation Statistics: 4 Vital Things Everyone Should Know - Unsupervised Machine Learning, STHDA. http://www.sthda.com/english/wiki/wiki.php?id_contents=7952
Reusova, A.: Hierarchical Clustering on Categorical Data in R, Medium, 1 April 2018. https://towardsdatascience.com/hierarchical-clustering-on-categorical-data-in-r-a27e578f2995
Filaire, T.: Clustering on mixed type data, Medium, 17 July 2018. https://towardsdatascience.com/clustering-on-mixed-type-data-8bbd0a2569c3
Bhalla, D.: A Complete Guide To Random Forest in R, Listen DATA, https://www.listendata.com/2014/11/random-forest-with-r.html#Random-Forest-R-Code
Labib, M.F., Rifat, A.S., Hossain M.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 (2019)
Shahriar, M.M., Iqubal, M.S., Mitra, S., Das, A.K.: A deep learning approach to predict malnutrition status of 0–59 month’s older children in Bangladesh. In: IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT 2019), BALI, Indonesia, pp. 145–149 (2019)
Sechidis, K., Sperrin, M., Petherick, E.S., Luján, M., Brown, G.: Dealing with under-reported variables: An information theoretic solution. Int. J. Approximate Reasoning 85, 159–177 (2017). https://doi.org/10.1016/j.ijar.2017.04.002
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Siam, Z.S. et al. (2020). Study of Machine Learning Techniques on Accident Data. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_3
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DOI: https://doi.org/10.1007/978-3-030-63119-2_3
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