Data-Driven Insights for Moroccan Airports: PCA and Clustering to Enhance Operational Performance

Following the trend of increasing complexity among systems, in an attempt to meet air passengers’ demands for higher quality service, this paper contributes to this stream of research by studying the operational efficiency of Moroccan airports through a novel multivariate approach. This research examines the following five performance metrics: baggage handling time, police screening time, customs processing time, passenger traffic, and flight delays. In this context and making use of Principal Component Analysis (PCA) with K-Means clustering, this paper aims at identifying the causes of operational variability, their significance in terms of performance management, and differentiating flights with similar operational profiles. Turning so particular techniques to the data of the moroccan airports this study reveals hidden patterns within airport interrelated activities, that in most cases were neglected by the traditional system of measurement. The findings make methodologies advancement in multivariate analysis of transport systems as well as practical improvement in the management of airport operations, and eventually impact on coordinated strategies of resource allocation for the systemic profit and the passenger utility. Through the use of PCA and K-means on the unreleased data of airports in Morocco, this paper is the first to offer a full multivariate study of the airport in the whole North African region. In contrast with standard monitoring systems which treat metrics as isolated entities, the study concurrently analyzes the dependencies among five key measures, discloses latent operational patterns, and promotes the formulation of context-based management policies suitable for an immature aviation market.

@article{Fatih2025,
  title     = {Data-Driven Insights for Moroccan Airports: PCA and Clustering to Enhance Operational Performance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {H. Fatih and A. Bentaleb and M. Lazaar and B. Bentalha},
  doi       = {10.14569/IJACSA.2025.01612112},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612112}
}