This new approach represents the transport system by connecting zones with similar circulation patterns, rather than relying solely on geographical proximity. The analysis, led by Javier Jesús Sánchez Medina, a researcher at ULPGC's Institute of Cybernetics, Business, and Society, and Leopoldo Acosta Sánchez from the Department of Computer Science and Systems at ULL, reveals hidden connections not evident in traditional maps.
The study, which utilized data from Transportes Interurbanos de Tenerife SA (TITSA), highlights that 85% of functional connections occur between zones separated by more than 5 kilometers. This methodology allows for the identification of operationally related areas, detection of incidents, and improvement of traffic condition prediction, offering a valuable tool for public transport management and planning.
“"The map shows us where the roads and bus lines are, but not necessarily how the network functions. By analyzing millions of real vehicle movements, we have discovered relationships between areas of Tenerife that are not visible from geography. This new way of looking at transport can open new avenues for improving its management and detecting problems earlier."
The authors note that this methodology is applicable to other transport networks in the Canary Islands and other territories. The results of this research have been published in the international journal Expert Systems with Applications, specializing in artificial intelligence and machine learning.
