Unravelling the spatial properties of individual mobility patterns using longitudinal travel data

Abstract

The analysis of longitudinal travel data enables investigating how mobility patterns vary across the population and identify the spatial properties thereof. The objective of this study is to identify the extent to which users explore different parts of the network as well as identify distinctive user groups in terms of the spatial extent of their mobility patterns. To this end, we propose two means for representing spatial mobility profiles and clustering travellers accordingly. We represent users patterns in terms of zonal visiting frequency profiles and grid-cells spatial extent heatmaps. We apply the proposed analysis to a large-scale multi-modal mobility dataset from the public transport system in Stockholm, Sweden. We unravel three clusters - Locals, Commuters and Explorers - that best describe the zonal visiting frequency and show that their composition varies considerably across users’ place of residence. We also identify 15 clusters of visiting spatial extent based on the intensity and direction in which they are oriented. A cross-analysis of the results of the two clustering methods reveals that user segmentation based on exploration patterns and spatial extent are largely independent, indicating that the two different clustering approaches provide fundamentally different insights into the underlying spatial properties of individuals’ mobility patterns. The approach proposed and demonstrated in this study could be applied for any longitudinal individual travel demand data.