Survey Paper on Analyze and Predict the Nature of Road Traffic Accidents using Data Mining Techniques in Maharashtra, India

Survey Paper on Analyze and Predict the Nature of Road Traffic Accidents using Data Mining Techniques in Maharashtra, India

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-53 Number-1
Year of Publication : 2017
Authors : Baye Atnafu, Gagandeep Kaur
DOI :  10.14445/22315381/IJETT-V53P206

Citation 

Baye Atnafu, Gagandeep Kaur "Survey Paper on Analyze and Predict the Nature of Road Traffic Accidents using Data Mining Techniques in Maharashtra, India", International Journal of Engineering Trends and Technology (IJETT), V53(1),23-31 November 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Traffic accidents are the main cause of death as well as serious injuries in the world. India is among the emerging countries where the rate at which traffic accident occurs is more than the critical limit. Due to this reason difficult to know the nature of road traffic accidents. As a human being, we all want to avoid traffic accidents and stay safe. In order to stay safe, careful analysis of roadway traffic accident data is important to identify the nature of traffic accident that causesfatal and series injuries. Analysis of road traffic accidents is significantto expose the association between the various types of factors thatinfluencethe nature of road traffic accidents. For this purpose, there are a number of classification and association rule mining algorithms available to analyze, detect and predict the road accident historical data and to obtained hidden patterns from huge data. From these, this survey paper discusses the algorithms and data mining tools that areproved better in theprevious studies.

Reference
1. WHO. Road traffic safety. Available from http://www.who.int/mediacentre/factsheets/fs358/en/ Accessed on 21 September 2017.
2. 400 road deaths per day in India; up 5% to 1.46 lakh in 2015 Available fromhttp://timesofindia.indiatimes.com/lifestyle/healthfitness/ health-news/400-road deaths-per-day-in-India-up-5- to-1-46-lakh-in-2015/articleshow/51920988.cms accessed on 21 September 2017.
3. Tavakoli Kashani, A., Shariat-Mohaymany, A., & Ranjbari, A. (2011). A data mining approach to identify key factors of traffic injury severity. PROMET-Traffic&Transportation, 23(1), 11-17.
4. Kumar, S., & Toshniwal, D. (2017). Severity analysis of powered two-wheeler traffic accidents in Uttarakhand, India. European Transport Research Review, 9(2), 24.
5. A. Garib, A.E Radwan and H. Al-Deek. Estimating Magnitudeand Duration of Incident Delays. Journal of Transportation Engineering, vol. 123, Nov./Dec. 1997, pp. 459-465.
6. Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1), 62-72.
7. Wang, Y., & Zhang, W. (2017). Analysis of Roadway and Environmental Factors Affecting Traffic Crash Severities. Transportation Research Procedia, 25, 2124-2130.
8. Mussone, L., Bassani, M., & Masci, P. (2017). Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis & Prevention, 103, 112-122.
9. Wu, Y., Abdel-Aty, M., & Lee, J. (2017). Crash risk analysis during fog conditions using real-time traffic data. Accident Analysis & Prevention.
10. Sachin Kumar, Durga Toshniwal, Manoranjan Parida. "A comparative analysis of heterogeneity in road accident data using data mining techniques", Evolving Systems, 2016.
11. George, Y., Athanasios, T., & George, P. (2017). Investigation of road accident severity per vehicle type. Transportation Research Procedia, 25, 2081-2088.
12. Hao, W., Kamga, C., Yang, X., Ma, J., Thorson, E., Zhong, M., & Wu, C. (2016). Driver injury severity study for truck involved accidents at highway-rail grade crossings in the United States. Transportation research part F: traffic psychology and behavior, 43, 379-386.
13. Naik, B., Tung, L. W., Zhao, S., & Khattak, A. J. (2016). Weather impacts on single-vehicle truck crash injury severity. Journal of safety research, 58, 57-65.
14. Delen, D., Tomak, L., Topuz, K., & Eryarsoy, E. (2017). Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. Journal of Transport & Health, 4, 118-131.
15. Li, L., Shrestha, S., & Hu, G. (2017, June). Analysis of road traffic fatal accidents using data mining techniques. In Software Engineering Research, Management and Applications (SERA), 2017 IEEE 15th International Conference on (pp. 363-370). IEEE.
16. Naqvi, H. M., & Tiwari, G. (2017). Factors Contributing to Motorcycle Fatal Crashes on National Highways in India. Transportation Research Procedia, 25, 2089-2102.
17. Velivela, G.,K Purna, P.,Challa, Y.,G Krishna, V., &Dr. S Krishna, R. (2017). Traffic Accidents Analysis with respect to Road user using data mining techniques.International Journal of Emerging Trends & Technology in Computer Science, 6(3),2278-6856.
18. Krishnaveni, S., & Hemalatha, M. (2011). A perspective analysis of traffic accident using data mining techniques. International Journal of Computer Applications, 23(7), 40-48.
19. Shanthi, S., & Ramani, R. G. (2011). Classification of vehicle collision patterns in road accidents using data mining algorithms. International Journal of Computer Applications, 35(12), 30-37.
20. El Tayeb, A. A., Pareek, V., & Araar, A. (2015). Applying association rules mining algorithms for traffic accidents in Dubai. International Journal of Soft Computing and Engineering.
21. Akomolafe, D. T., & Olutayo, A. (2012). Using Data Mining Technique to Predict Cause of Accident and Accident Prone Locations on Highways. American Journal of Database Theory and Application, 1(3), 26-38.
22. Guan, L., Liu, W., Yin, X., & Zhang, L. (2010, May). Traffic incident duration prediction based on Artificial Neural Network. In Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on (Vol. 3, pp. 1076-1079). IEEE.
23. Sohn, S. Y., & Shin, H. (2001). Pattern recognition for road traffic accident severity in Korea. Ergonomics, 44(1), 107- 117.
24. Kumar, S., & Toshniwal, D. (2015). A data mining framework to analyze road accident data. Journal of Big Data, 2(1), 26.
25. Shanthi, S., & Ramani, R. G. (2012, March). Gender-specific classification of road accident patterns through data mining techniques. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 359-365). IEEE.
26. Mujalli, R. O., López, G., & Garach, L. (2016). Bayes classifiers for imbalanced traffic accidents datasets. Accident Analysis & Prevention, 88, 37-51.
27. Sayed, T., Abdelwahab, W., & Navin, F. (1995). Identifying accident-prone locations using fuzzy pattern recognition. Journal of Transportation Engineering, 121(4), 352-358.
28. Classification, prediction, and clustering. Available from http://shareengineer.blogspot.in/2012/09/classification-andclustering. htmlaccessedAccessed on 22 September 2017.
29. Sewaiwar, Purva, and Kamal Kant Verma. "Comparative Study of Various Decision Tree Classification Algorithm Using WEKA." International Journal of Emerging Research in Management &Technology 4 (2015): 2278-9359.
30. Rapidminer.Randomtree. Available from https://docs.rapidminer.com/studio/operators/modeling/predi ctive/trees/random_tree.htmlaccessed 22 September 2017.
31. Zhao, Yongheng, and Yanxia Zhang. "Comparison of decision tree methods for finding active objects." Advances in Space Research 41.12 (2008): 1955-1959.
32. Lai, K., & Cerpa, N. (2001, October). Support vs. confidence in association rule algorithms. In Proceedings of the OPTIMA Conference, Curicó.
33. Aher, S. B., & Lobo, L. M. R. J. (2012). A comparative study of association rule algorithms for course recommender system in e-learning. International Journal of Computer Applications, 39(1), 48-52.
34. L. Zhichun and Y. Fengxin, “An improved frequent pattern tree growth algorithm,” Applied Science and Technology, vol. 35, no. 6, pp. 47–51, 2008.
35. C. Jun and G. Li, “An improved FP-growth algorithm based on item head table node,” Information Technology, vol. 12, pp. 34–35, 2013.
36. B. Zheng and J. Li, “An improved algorithm based on FPgrowth,” Journal of Pingdingshan Institute of Technology, vol. 17, no. 4, pp. 9–12, 2008.
37. National Highway Authority of India.Regional Office – Mumbai. Available from http://nhai.org.inaccessed on July 30, 2017.

Keywords
data mining, random tree, J48, Naive Baye’s, association rule mining, road accidents.