Unravelling the Spatial Properties of Individual Mobility Patterns using Longitudinal Travel Data

TL;DR

This study analyzes longitudinal travel data to uncover how individuals explore spatial areas, identifying distinct user groups and mobility patterns using clustering techniques on a large-scale public transport dataset from Stockholm.

Contribution

It introduces two novel methods for representing and clustering individual mobility patterns, revealing diverse user groups and spatial behaviors in longitudinal travel data.

Findings

Identified three main user clusters: locals, commuters, explorers.

Discovered 18 clusters of spatial extent with four overarching shape groups.

Demonstrated the applicability of the approach to various longitudinal travel datasets.

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 data set 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 and related demographics. We also identify 18 clusters of visiting spatial extent which form four groups that follow similar shapes of travel extent yet oriented in different directions. The approach proposed and applied in this study could be applied for any longitudinal individual travel demand data.