ArticleScientific reports2015
Explaining the power-law distribution of human mobility through transportation modality decomposition.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- Time Profile of U.S. Neighborhoods: Datasets of Time Use at Social Infrastructure Places.Scientific data · 2025Article
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- Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective.Scientific reports · 2024Article
- Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units.Scientific reports · 2023Article
- Uncovering spatial and social gaps in rural mobility via mobile phone big data.Scientific reports · 2023Article
- Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission.Nano communication networks · 2022Article
- Spatial scales in human movement between reservoirs of infection.Journal of theoretical biology · 2021Article
- Deriving fine-scale models of human mobility from aggregated origin-destination flow data.PLoS computational biology · 2021Article
- Universal scaling laws of collective human flow patterns in urban regions.Scientific reports · 2020Article
- The scales of human mobility.Nature · 2020Article
- Mapping global variation in human mobility.Nature human behaviour · 2020Article
- Estimation of the shared mobility demand based on the daily regularity of the urban mobility and the similarity of individual trips.PloS one · 2020Article
- Using continuous sensor data to formalize a model of in-home activity patterns.Journal of ambient intelligence and smart environments · 2020Article
- Evidence to support common application switching behaviour on smartphones.Royal Society open science · 2019Article
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- Characterizing preferred motif choices and distance impacts.PloS one · 2019Article
- Tracking random walks.Journal of the Royal Society, Interface · 2018Article
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5 authors.
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Abstract
Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.
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