H. Anandakumar , Abishek Sailesh, C. Muthumeenal, S. Visalakshi, K. Muthumani, Akshaya College of Engineering and Technology, Coimbatore, Tamilnadu, India.
International Journal of Advanced Information and Communication Technology
Received On : December 17, 2020
Revised On : January 18, 2020
Accepted On : February 14, 2020
Published On : March 05, 2020
Volume 07, Issue 03
Pages : 037-040
Abstract
In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different traffic and context model form distributed traffic patterns. One major challenge in predicting traffic is how much to rely on the prediction model constructed using historical data in the real-time traffic situation, which may differ from that of the historical data due to the fact that traffic situations are numerous and changing over time. The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together to improve the learning accuracy of classifier. The conducted experimental results on prediction of traffic dataset prove that the proposed algorithm significantly outperforms the existing algorithm.
Keywords
Spatiotemporal; Data mining; Traffic Prediction; Clustering; Apriori; Sequence pattern mining.
Cite this article
H. Anandakumar, Abishek Sailesh, C. Muthumeenal, S. Visalakshi, K. Muthumani, “Spatiotemporal Traffic Analysis using Big Data,” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 37–40, Feb. 2020.
Copyright
© 2020 H. Anandakumar, Abishek Sailesh, C. Muthumeenal, S. Visalakshi, K. Muthumani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.