Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity
Licentiatavhandling, 2020

Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2°C scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited.

This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science.

Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning.

travel mode

data mining

travel time


social media data

gravity model


geographical information systems

Opponent: Prof. Sarah Williams, Associate Professor of Technology and Urban Planning; Director, Civic Data Design Lab, Massachusetts Institute of Technology


Yuan Liao

Chalmers, Rymd-, geo- och miljövetenskap, Fysisk resursteori

Yuan Liao, Sonia Yeh and Jorge Gil (4th Mar. 2020). Feasibility of estimating travel demand using social media data

Disparities in travel times between car and transit: Spatiotemporal patterns in cities

Scientific Reports,; Vol. 10(2020)

Artikel i vetenskaplig tidskrift

Hållbara städer: användande av stora datamängder för att förstå och hantera rörelsemönster och trafikstockningar

Formas (2016-01326), 2017-01-01 -- 2019-12-31.


Annan data- och informationsvetenskap


Transportteknik och logistik


Informations- och kommunikationsteknik




Hållbar utveckling




Opponent: Prof. Sarah Williams, Associate Professor of Technology and Urban Planning; Director, Civic Data Design Lab, Massachusetts Institute of Technology

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