Following the footprints of visitors

Abstract

Visitors’ induced seasonal demand spikes make supply dimensioning a challenging task. Smart card data have been widely used for exploring segmented passenger travel behavior; however, their potential in the tourism context remains underexplored. In this study, we portray distinct temporal and spatial visitor profiles in the Stockholm region by means of smart card data analytics. Empirical understanding of visitors’ travel patterns can inform public transportation planning in relation to tourist destinations and contribute to the strategic assessment and refinement of policies such as fare structures. In our analysis, visitors are defined using a combination of criteria, including: i) one-time users with a travel span of less than a week (activity criterion), and ii) users utilizing dedicated visitor fare products (fare product criterion). We apply k-means clustering and identify four activity clusters, namely Multi-travel short-stay visitors, Long-stay and frequent travellers, Day trippers, and Evening visitors, as well as three spatial visitor clusters denominated as City center visitors, Outside the city center visitors, and Mixed visitors. The clustering reveals that Day trippers dominate the activity segmentation, accounting for 38.5% of visitors, while the spatial segmentation results in three more evenly distributed clusters. Notably, 37.3% of visitors primarily utilize the public transportation system outside the city center, highlighting the need for policy attention beyond central areas – for example, accessibility improvements for less accessible visitor destinations. Last, our framework is fully reproducible, demonstrating the potential of smart card data in exploring visitors’ travel behavior.