Identification and Characterisation of Passenger Archetypes based on annual Long-distance Travel Patterns

The European transport policy envisions a multimodal transport system where different networks and services are planned and managed in a coordinated manner to maximize the efficiency, predictability, environmental sustainability, and resilience of the door-to-door passenger journey. To achieve this goal, transport planners need an in-depth understanding of the behaviors, preferences and needs of the different types of travelers within Europe. This paper presents a methodology for the identification and characterization of long-distance passenger archetypes based on the application of unsupervised learning algorithms to a set of travel behavior indicators extracted from anonymized mobile network data. The proposed methodology is demonstrated and evaluated through its application to long-distance travel in Spain.