  It is crucial for the service provider to comprehend and forecast mobile
traffic in large-scale cellular networks in order to govern and manage
mechanisms for base station placement, load balancing, and network planning.
The purpose of this article is to extract and simulate traffic patterns from
more than 14,000 cells that have been installed in different metropolitan
areas. To do this, we create, implement, and assess a method in which cells are
first categorized by their point of interest and then clustered based on the
temporal distribution of cells in each region. The proposed model has been
tested using real-world 5G mobile traffic datasets collected over 31 weeks in
various cities. We found that our proposed model performed well in predicting
mobile traffic patterns up to 2 weeks in advance. Our model outperformed the
base model in most areas of interest and generally achieved up to 15\% less
prediction error compared to the na\"ive approach. This indicates that our
approach is effective in predicting mobile traffic patterns in large-scale
cellular networks.
