Road Accidents Analysis – A Survey
DOI:
https://doi.org/10.61841/nksbdz49Keywords:
road accident,, factors causing accident, data mining techniques, severityAbstract
In India, road injuries are one of the top four causes of death and health loss among persons of age group 15-49 years. Road accidents also lead to economic loss to the country. The aim of this survey is to investigate the factors leading to road accidents and hence analyse the various algorithms to predict the severity of an accident.
Downloads
References
1. Sanjay, PR, “400 deaths a day are forcing India to take car safety seriously” The Economic Times 10th January 2018. Published: www.economictimes.indiatimes.co/news/politics-and-nation/. Web.
2. Government of India, Ministry of Road Transport and Highways, Transport Research Wing. (2017). Road Accidents in India – 2017.Retrieved from www.indiaenvironmentportal.org.in
3. Alireza Pakgohar, Reza Sigari Tabrizi, Mohadeseh Khalili, Alireza Esmaeili, The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach, Procedia Computer Science, Volume 3, 2011, Pages 764-769, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2010.12.126.
4. T. Gang, S. Huan-Sheng, Y. Yong-Gang and M. Jafari, "Cause Analysis of Traffic Accidents Based on Degrees of Attribute Importance of Rough Set," 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), Beijing, 2015, pp. 1665-1669. doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.303
5. Vasavi S., Mishra D., Azar A., Joshi A. Extracting Hidden Patterns Within Road Accident Data Using Machine Learning Techniques. Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 625. Springer, Singapore
6. Kumar, S. & Toshniwal, D. J. Mod. Transport. (2016) 24: 62. https://doi.org/10.1007/s40534-016-0095-5
7. G. Kaur and E. H. Kaur, "Prediction of the cause of accident and accident prone location on roads using data mining techniques," 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, 2017, pp. 1-7. doi: 10.1109/ICCCNT.2017.8204001
8. Q. Liyan and S. Chunfu, "Macro Prediction Model of Road Traffic Accident Based on Neural Network and Genetic Algorithm," 2009 Second International Conference on Intelligent Computation Technology and Automation, Changsha, Hunan, 2009, pp. 354-357. doi: 10.1109/ICICTA.2009.93
9. R. Yu and X. Liu, "Study on Traffic Accidents Prediction Model Based on RBF Neural Network," 2010 2nd International Conference on Information Engineering and Computer Science, Wuhan, 2010, pp. 1-4. doi: 10.1109/ICIECS.2010.5678126
10. Galal A. Ali, Awadalla Tayfour, “Characteristics and Prediction of Traffic Accident Casualties In Sudan Using Statistical Modeling and Artificial Neural Networks”, International Journal of Transportation Science and Technology, Volume 1, Issue 4, 2012, Pages 305-317, ISSN 2046-0430, https://doi.org/10.1260/2046-0430.1.4.305.
11. C. Sugetha, L. Karunya, E. Prabhavathi and P. K. Sujatha, "Performance Evaluation Of Classifiers For Analysis Of Road Accidents," 2017 Ninth International Conference on Advanced Computing (ICoAC), Chennai, 2017, pp. 365-368. doi: 10.1109/ICoAC.2017.8441188
12. T.K.Bahiru, D. Kumar Singh and E.A. Tessfaw, "Comparative Study on Data Mining Classification Algorithms for Predicting Road Traffic Accident Severity," 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2018, pp. 1655-1660. doi: 10.1109/ICICCT.2018.8473265
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
