Classifying Road Traffic Data Using Data Mining Classification Algorithms: A Comparative Study

  • J. Patricia Annie JebamalarEmail author
  • Sujni Paul
  • D. Ponmary Pushpa Latha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)


People move from place to place for various purposes using different modes of transportation. This creates traffic on the roads. As population increases, number of vehicles on the road increases. This leads to a serious problem called traffic congestion. Predicting traffic congestion is a challenging task. Data Mining analyzes huge data to produce meaningful information to the end users. Classification is a function in data mining which classifies the given data into various classes. Traffic congestion on roads can be classified as free, low, medium, high, very high, and extreme. Congestion on roads is based on the attributes such as speed of the vehicle, density of vehicles on the road, occupation of the road by the vehicles, and the average waiting time of the vehicles. This paper discusses how traffic congestion is predicted using data mining classifiers with big data analytics and compares different classifiers and their accuracy.


Data mining Classification Traffic congestion Accuracy Big data analytics 


  1. 1.
    Ananth, G.S., Raghuveer, K.: A novel approach of using MongoDB for big data analytics. Int. J. Innovative Stud. Sci. Eng. Technol. (IJISSET) 3(8), 7 (2017). ISSN 2455-4863(Online)Google Scholar
  2. 2.
    Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865 (2015)Google Scholar
  3. 3.
    Kaur, G., Chhabra, A.: Improved J48 classification algorithm for the prediction of diabetes. Int. J. Comput. Appl. 98(22), 0975–8887 (2014)Google Scholar
  4. 4.
    Rajeshinigo, D., J. Patricia Annie Jebamalar: Educational Mining: A Comparative Study of Classification Algorithms Using Weka. Innovative Res. Comput. Commun. Eng. (2017)Google Scholar
  5. 5.
    Kaur, P., Singh, M., Josan, G.S.: Classification and prediction based data mining algorithms to predict slow learners in education sector. In: 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015). Procedia Comput. Sci. 57, 500–508. Elsevier (2015)Google Scholar
  6. 6.
    Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., Honrao, V.: Predicting students’ performance using ID3 and C4.5 classification algorithms. Int. J. Data Min. Knowl. Manage. Process (IJDKP) 3(5) (2013).
  7. 7.
    Iyer, A., Jeyalatha, S., Sumbaly, R.: Diagnosis of diabetes using classification mining techniques. Int. J. Data Min. Knowl. Manage. Process (IJDKP) 5(1) (2015).
  8. 8.
    Vijayarani, S., Dhayanand, S.: Data mining classification algorithms for kidney disease prediction. Int. J. Cybern. Inf. (IJCI) 4(4) (2015).
  9. 9.
    Fouladgar, M., Parchami, M., Elmasri, R., Ghaderi, A.: Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction (2017)Google Scholar
  10. 10.
    Raiyn, J., Qasemi, A., El Gharbia, B.: Road traffic congestion management based on a search-allocation approach. Transp. Telecommun. 18(1), 25–33 (2017). Scholar
  11. 11.
    Rao, A.M., Rao, K.R.: Measuring urban traffic congestion—a review. Int. J. Traffic Transp. Eng. (2012)Google Scholar
  12. 12.
    Bauza, R., Gozalvez, J., Sanchez-Soriano, J.: Road traffic congestion detection through cooperative vehicle-to-vehicle communications. In: Proceedings of the 2010 IEEE 35th Conference on Local Computer Networks, pp. 606–612 (2010)Google Scholar
  13. 13.
    Salvithal, N.N., Kulkarni, R.B.: Evaluating performance of data mining classification algorithm in weka. Int. J. Appl. Innov. Eng. Manage. (IJAIEM) 2(10), 273–281(2013). ISSN 2319 – 4847Google Scholar
  14. 14.
    Akhila, G.S., Madhu, G.D., Madhu, M.H., Pooja, M.H.: Comparative study of classification algorithms using data mining. Discov. Sci. 9(20), 17–21 (2014)Google Scholar
  15. 15., SUMO tutorial

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • J. Patricia Annie Jebamalar
    • 1
    Email author
  • Sujni Paul
    • 2
  • D. Ponmary Pushpa Latha
    • 3
  1. 1.Karunya UniversityCoimbatoreIndia
  2. 2.School of Engineering and Information TechnologyAl Dar University CollegeDubaiUAE
  3. 3.School of Computer Science and TechnologyKarunya UniversityCoimbatoreIndia

Personalised recommendations