Improving the Trajectory Clustering using Meta-Heuristic Algorithms

The rapid growth of GPS trajectories obscures valuable information regarding urban road infrastructure, urban traffic patterns, and population mobility. An innovative method termed trajectory regression clustering is introduced to improve the extraction of hidden data and generate more precise clustering results. This approach belongs to the unsupervised trajectory clustering category and has the objective of minimizing the loss of local information inside the trajectory. It also seeks to prevent the algorithm from getting stuck in a suboptimal solution. The methodology we employ consists of three primary stages. To begin with, we present the notion of trajectory clustering and devise a distinctive approach known as angle-based partitioning to segment line segments. The evaluation results indicate a significant improvement in the clustering accuracy of the proposed method compared to existing methodologies, especially for a high number of clusters. The HCMGA and HCMMOPSO algorithms have improved clustering accuracy for MBP values by 0.61% and 0.64%, respectively, as compared to previous approaches. Moreover, based on the implementation findings, the ant colony approach demonstrates superior accuracy compared to alternative methods, while the particle swarm method exhibits faster convergence.

@article{Li2024,
  title     = {Improving the Trajectory Clustering using Meta-Heuristic Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Haiyang Li and Xinliu Diao},
  doi       = {10.14569/IJACSA.2024.0150126},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150126}
}