Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations
Doktorsavhandling, 2021

Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport.

This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1).

Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport.

big trip data

traffic data

transport modes

mobility models

geographical information systems

data mining

social media data

Online
Opponent: Prof. Michael Batty, Emeritus Professor of Planning, Centre for Advanced Spatial Analysis, The Bartlett Faculty of the Built Environment, University College London, UK

Författare

Yuan Liao

Chalmers, Rymd-, geo- och miljövetenskap, Fysisk resursteori, Fysisk resursteori 2

Feasibility of estimating travel demand using geolocations of social media data

Transportation,; Vol. In Press(2021)

Artikel i vetenskaplig tidskrift

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

Scientific Reports,; Vol. 10(2020)

Artikel i vetenskaplig tidskrift

Y. Liao, K. Ek, E. Wennerberg, S. Yeh and J. Gil (26th Apr. 2021). A mobility model for synthetic travel demand from sparse in-dividual traces. Submitted to Computers Environment and Urban Systems, Under review.

Y. Liao (11th May 2021). Ride-sourcing compared to its public-transit alternative using big trip data. Submitted to Journal of Transport Geography, Under review.

Have you ever used social media apps on your phone, e.g., Twitter? Have you tried some new ways of travelling, e.g., Uber? Do you know what we can do with the data collected from these digital platforms? The prevalence of digital technologies allows us to collect people’s movements on a large scale. And they are valuable sources for improving our understanding of mobility patterns and how different transport modes support people’s movements.

This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from a transportation network company, to answer two research questions:

- What are the potentials and limitations of using these emerging data sources for modelling mobility?

- How can these new data sources be properly modelling for characterising transport modal disparities?

Despite organised around two questions, this thesis thematically tells a single story from understanding to applying. It starts from the fundamental aspects of mobility (Paper I), a more systematic exploration of the data feasibility for travel demand estimation (Paper II), towards a more practical direction – addressing the identified issue of data sparsity with a new model for synthetic travel demand (Paper III). Involving more diverse data sources beyond geolocations of people's movements, the research continues with putting the movements of people into its context, transport systems; Paper IV-V provide the insights into the disparities between car and public transit with a high spatiotemporal resolution, which are useful for guiding the real-world practices, such as transport planning for public transit and encouraging a modal shift from car to public transit.

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

Formas, 2017-01-01 -- 2019-12-31.

Drivkrafter

Hållbar utveckling

Styrkeområden

Transport

Energi

Ämneskategorier

Transportteknik och logistik

Kulturgeografi

Miljövetenskap

ISBN

978-91-7905-508-0

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4975

Utgivare

Chalmers tekniska högskola

Online

Online

Opponent: Prof. Michael Batty, Emeritus Professor of Planning, Centre for Advanced Spatial Analysis, The Bartlett Faculty of the Built Environment, University College London, UK

Mer information

Senast uppdaterat

2021-05-28