Conserved quantities in human mobility

From locations to trips

Journal Article (2023)
Author(s)

Ye Hong (ETH Zürich)

Henry Martin (Institute of Advanced Research in Artificial Intelligence (IARAI), ETH Zürich)

Yanan Xin (ETH Zürich)

Dominik Bucher (c.technology, ETH Zürich)

Daniel J. Reck (ETH Zürich)

Kay W. Axhausen (ETH Zürich)

Martin Raubal (ETH Zürich)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1016/j.trc.2022.103979 Final published version
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Publication Year
2023
Language
English
Affiliation
External organisation
Journal title
Transportation Research Part C: Emerging Technologies
Volume number
146
Article number
103979
Downloads counter
186

Abstract

Quantifying intra-person variability in travel choices is essential for the comprehension of activity–travel behaviour. Due to a lack of empirical studies, there is limited understanding of how an individual's travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼3800 individuals to analyse individuals’ activity–travel behaviour over the long term. The general movement patterns of the considered population are characterised using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ∼15 mode–location combinations, of which ∼7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity–travel behaviour and open new possibilities for designing mobility simulation models.