A Data-mining Based System for Identifying the Injury Severity in Road Accidents
DOI:
https://doi.org/10.13052/jrss0974-8024.1827Keywords:
Road accident, data mining, prediction, modellingAbstract
Road accidents have been among the leading reasons for injury and death globally; this study aims to develop a set of rules that Indian road traffic and safety agencies can specifically utilize to find out the potential reasons leading to accident severity. In this study we used R software to establish classification models -Multi-Layer Perceptron (MLP), Naive Bayes, decision trees, and logistic regression that can accurately predict the severity of injuries. By adopting 2585 road accident records in India from 2013 to 2018, our analysis reveals that the overall accuracy of logistic regression 87.47%, decision tree 91.21%, and MLP 86.05% in predicting injury severity However, Naive Bayes model demonstrated lower accuracy at 75.57% compared to the other algorithms. Finally, to identify the significant factors influencing accident severity, we have further explored the rules by the decision tree algorithm, and based on the findings, highlighted rules and focus areas to reduce the accident severity
Downloads
References
J. Abellán, G. López, and J. De Oña, “Analysis of traffic accident severity using decision rules via decision trees,” Expert Systems with Applications, vol. 40, no. 15, pp. 6047–6054, 2013.
L. Y. Chang and J. T. Chien, “Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model,” Safety Science, vol. 51, no. 1, pp. 17–22, 2013.
L. Y. Chang and H. W. Wang, “Analysis of traffic injury severity: An application of non-parametric classification tree techniques,” Accident Analysis & Prevention, vol. 38, no. 5, pp. 1019–1027, 2006.
D. Delen, R. Sharda, and M. Bessonov, “Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks,” Accident Analysis & Prevention, vol. 38, no. 3, pp. 434–444, 2006.
S. Haykin, Neural Networks: A Comprehensive Foundation, Upper Saddle River, NJ: Prentice Hall PTR, 1998.
A. T. Kashani and A. S. Mohaymany, “Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models,” Safety Science, vol. 49, no. 10, pp. 1314–1320, 2011.
A. T. Kashani, R. Rabieyan, and M. M. Besharati, “A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers,” Journal of Safety Research, vol. 51, pp. 93–98, 2014.
S. Kumar and D. Toshniwal, “A data mining framework to analyze road accident data,” Journal of Big Data, vol. 2, no. 1, pp. 1–18, 2015.
O. H. Kwon, W. Rhee, and Y. Yoon, “Application of classification algorithms for analysis of road safety risk factor dependencies,” Accident Analysis & Prevention, vol. 75, pp. 1–15, 2015.
F. L. Mannering, V. Shankar, and C. R. Bhat, “Unobserved heterogeneity and the statistical analysis of highway accident data,” Analytic Methods in Accident Research, vol. 11, pp. 1–16, 2016.
M. Megnidio-Tchoukouegno and J. A. Adedeji, “Machine learning for road traffic accident improvement and environmental resource management in the transportation sector,” Sustainability, vol. 15, no. 3, p. 2014, 2023.
S. Moslem et al., “A novel spherical decision-making model for measuring the separateness of preferences for drivers’ behavior factors associated with road traffic accidents,” Expert Systems with Applications, vol. 238, p. 122318, 2024.
R. O. Mujalli and J. De Oña, “A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks,” Journal of Safety Research, vol. 42, no. 5, pp. 317–326, 2011.
C. D. Newgard, R. J. Lewis, and B. T. Jolly, “Use of out-of-hospital variables to predict severity of injury in pediatric patients involved in motor vehicle crashes,” Annals of Emergency Medicine, vol. 39, no. 5, pp. 481–491, 2002.
S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, no. 3, pp. 660–674, 1991.
M. Taamneh, S. Alkheder, and S. Taamneh, “Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates,” Journal of Transportation Safety & Security, vol. 9, no. 2, pp. 146–166, 2017.
World Health Organization, The Global Status on Road Safety, 2018, ISBN: 9789241565684. [Online]. Available: https://www.who.int/publications/i/item/9789241565684.
J. Wu, S. Rasouli, J. Zhao, Y. Qian, and L. Cheng, “Large truck fatal crash severity segmentation and analysis incorporating all parties involved: A Bayesian network approach,” Travel Behaviour and Society, vol. 30, pp. 135–147, 2023.
H. Zhang, “The optimality of naive Bayes,” Aa, vol. 1, no. 2, pp. 3–, 2004.
S. Zhu, “Analysis of the severity of vehicle-bicycle crashes with data mining techniques,” Journal of Safety Research, vol. 76, pp. 218–227, 2021.
A. N. Tripathi and B. Sharma, “Evaluation of a probabilistic framework for traffic volume forecasting using deep learning and traditional models,” International Journal of Experimental Research and Review, vol. 45, pp. 237–250, Nov. 2024.
