Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Predicting Accidental Locations of Dhaka-Aricha Highway in Bangladesh using Different Data Mining Techniques

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Md. Shahriare Satu, Tania Akter, Md. Sadrul Arifen, Md. Raza Mia
10.5120/ijca2017914096

Md. Shahriare Satu, Tania Akter, Md. Sadrul Arifen and Md. Raza Mia. Predicting Accidental Locations of Dhaka-Aricha Highway in Bangladesh using Different Data Mining Techniques. International Journal of Computer Applications 165(12):1-6, May 2017. BibTeX

@article{10.5120/ijca2017914096,
	author = {Md. Shahriare Satu and Tania Akter and Md. Sadrul Arifen and Md. Raza Mia},
	title = {Predicting Accidental Locations of Dhaka-Aricha Highway in Bangladesh using Different Data Mining Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {12},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume165/number12/27622-2017914096},
	doi = {10.5120/ijca2017914096},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Road traffic accident is one of the most leading issues which is concerned in many other countries like Bangladesh. Data mining is considered as a reliable technique to analyze traffic accident record and identify factors that provide severity of an accident. The goal of this research to analyze and build classification model that predict an accidental location in the Dhaka-Aricha highway. So, road accidental data is collected from different highway police stations which keep traffic accident record of every road traffic accident on this road. Then, raw dataset is preprocessed and build a classification model with five data mining classification algorithms named Rotation Forest, NBTree, JRip, Naive Bayes and Ridor that analyze traffic accident records to predict risky accidental locations. After classifying this dataset, accuracies of classifiers are compared and the best outcome is showed among them. This results can be used to prevent road accidents in the areas and overcome the number of accidents on the Dhaka-Aricha highway.

References

  1. Study: Road accidents killed one per hour in 2014. "http:// archive.dhakatribune.com/bangladesh/2015/apr/ 03/study-road-accidents-killed-one-hour-2014", March 2017.
  2. Sachin Kumar and Durga Toshniwal. Analysing road accident data using association rule mining. In Computing, Communication and Security (ICCCS), 2015 International Conference on, pages 1–6. IEEE, 2015.
  3. I.H.Witten, E. Frank, and M.A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science, 2011.
  4. So Young Sohn and Hyungwon Shin. Pattern recognition for road traffic accident severity in korea. Ergonomics, 44(1):107–117, 2001.
  5. Miao M Chong, Ajith Abraham, and Marcin Paprzycki. Traffic accident analysis using machine learning paradigms. Informatica (Slovenia), 29(1):89–98, 2005.
  6. Tibebe Beshah, Dejene Ejigu, Ajith Abraham, Vaclav Snasel, and Pavel Kromer. Pattern recognition and knowledge discovery from road traffic accident data in ethiopia: Implications for improving road safety. In Information and Communication Technologies (WICT), 2011 World Congress on, pages 1241–1246. IEEE, 2011.
  7. S Shanthi and R Geetha Ramani. Feature relevance analysis and classification of road traffic accident data through data mining techniques. In Proceedings of the World Congress on Engineering and Computer Science, volume 1, pages 24–26, 2012.
  8. Humberto Gonzalez. Finding patterns in 2013 road accident data in united kingdom. 2015.
  9. Sachin Kumar and Durga Toshniwal. A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1):62–72, 2016.
  10. An Shi, Zhang Tao, Zhang Xinming, and Wang Jian. Evolution of traffic flow analysis under accidents on highways using temporal data mining. In Intelligent Systems Design and Engineering Applications (ISDEA), 2014 Fifth International Conference on, pages 454–457. IEEE, 2014.
  11. SM Sohel Mahmud, Md Shamsul Hoque, and QA Shakur. Road safety research in bangladesh: constraints and requirements. In The 4th Annual paper meet (APM) and the 1st Civil Engineering Congress, organized by Civil Engineering Division Institution of Engineers, Bangladesh (IEB), Session V: Transportation Engineering-II, pages 22–24, 2011.
  12. SM Sohel Mahmuda, Ishtiaque Ahmedb, and Md Shamsul Hoquec. Road safety problems in bangladesh: Achievable target and tangible sustainable actions. 2014.
  13. Md Shamsul Hoque, Shah Md Muniruzzaman, and SN Ahmed. Performance evaluation of road safety measures: a case study of the dhaka-aricha highway in bangladesh. Transport and communications bulletin for Asia and the Pacific, 74, 2005.
  14. Juan Jos´e Rodriguez, Ludmila I Kuncheva, and Carlos J Alonso. Rotation forest: A new classifier ensemble method. IEEE transactions on pattern analysis and machine intelligence, 28(10):1619–1630, 2006.
  15. Tadeusz Lasota, Tomasz Luczak, and Bogdan Trawi´nski. Investigation of rotation forest method applied to property price prediction. In International Conference on Artificial Intelligence and Soft Computing, pages 403–411. Springer, 2012.
  16. Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine learning, 29(2-3):131–163, 1997.
  17. Yumin Zhao, Zhendong Niu, and Xueping Peng. Research on data mining technologies for complicated attributes relationship in digital library collections. Applied Mathematics & Information Sciences, 8(3):1173, 2014.
  18. Vaishali S Parsania, NN Jani, and Navneet H Bhalodiya. Applying na¨ive bayes, bayesnet, part, jrip and oner algorithms on hypothyroid database for comparative analysis.
  19. V Veeralakshmi and D Ramyachitra. Ripple down rule learner (ridor) classifier for iris dataset. Issues, 1(1):79–85.
  20. SR Kalmegh and SN Deshmukh. Categorical identification of indian news using j48 and ridor algorithm.
  21. A Sudha, P Gayathri, and N Jaisankar. Effective analysis and predictive model of stroke disease using classification methods. International Journal of Computer Applications, 43(14):26–31, 2012.
  22. Jiawei Han, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
  23. Cyril Goutte and Eric Gaussier. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In European Conference on Information Retrieval, pages 345–359. Springer, 2005.
  24. Cohen’s kappa. "https://en.wikipedia.org/wiki/ Cohen%27s_kappa", April 2017.
  25. Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. Data Mining: Practical machine learning tools and techniques. Elsevier, 2017.
  26. Eyad Abdullah and Ahmed Emam. Traffic accidents analyzer using big data. In 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pages 392–397. IEEE, 2015.

Keywords

Road Traffic Accident, Traffic Accident Record, Highway, Classification, Data Mining