Online Incremental Rough Set Learning in Intelligent Traffic System

In the last few years, vehicle to vehicle communication (V2V) technology has been developed to improve the efficiency of traffic communication and road accident avoidance. In this paper, we have proposed a model for online rough sets learning vehicle to vehicle communication algorithm. This model is an incremental learning method, which can learn data object-by-object or class-by-class. This paper proposed a new rules generation for vehicle data classifying in collaborative environments. ROSETTA tool is applied to verify the reliability of the generated results. The experiments show that the online rough sets based algorithm for vehicle data classifying is suitable to be executed in the communication of traffic environments. The implementation of this model on the objectives’ (cars’) rules that define parameters for the determination of the value of communication, and for reducing the decision rules that leads to the estimation of their optimal value. The confusion matrix is used to assess the performance of the chosen model and classes (Yes or No). The experimental results show the overall accuracy (predicted and actual) of the proposed model. The results show the strength of the online learning model against offline models and demonstrate the importance of the accuracy and adaptability of the incremental learning in improving the prediction ability.

@article{Bentaher2018,
  title     = {Online Incremental Rough Set Learning in Intelligent Traffic System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {3},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Amal Bentaher and Yasser Fouad and Khaled Mahar},
  doi       = {10.14569/IJACSA.2018.090312},
  url       = {https://doi.org/10.14569/IJACSA.2018.090312}
}