Computational Framework for Analytical Operation in Intelligent Transportation System using Big Data

Intelligent Transportation System (ITS) is the future of the current transport scheme. It is meant to incorporate an intelligent traffic management operation to offer vehicles more safety and valuable traffic-related information. A review of existing approaches showcases the implementation of varied scattered schemes where analytical operation is mainly emphasized. However, some significant shortcomings are witnessed in efficiently managing complex traffic data. Therefore, the proposed system introduces a novel computational framework with a joint operation toward analytical processing using big data targeting to manage raw and complex traffic data efficiently. As a novel feature, the model introduces a data manager who can manage the complex traffic stream, followed by decentralized traffic management, that can identify and eliminate artefacts using statistical correlation. Finally, predictive modelling is incorporated to offer knowledge discovery with the highest accuracy. The simulation outcome shows that Random Forest excels with 99% accuracy, which is the highest of all existing machine learning approaches, along with the accomplishment of 11.77% reduced overhead, 1.3% of reduced delay, and 67.47% reduced processing time compared to existing machine learning approaches.

@article{G2023,
  title     = {Computational Framework for Analytical Operation in Intelligent Transportation System using Big Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mahendra G and Roopashree H. R},
  doi       = {10.14569/IJACSA.2023.0140744},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140744}
}