Feasibility of estimating travel demand using geolocations of social media data

Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of...

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Bibliographic Details
Published in:	Transportation (Dordrecht) 2022-02, Vol.49 (1), p.137-161
Main Authors:	Liao, Yuan, Yeh, Sonia, Gil, Jorge
Format:	Article
Language:	eng
Subjects:	Attraction Commuting Demographics Demography Digital media Economic Geography Economics Economics and Finance Engineering Economics Estimation Feasibility Feasibility studies Generators gravity model Innovation/Technology Management lateral data Logistics longitudinal data Marketing Organization origin-destination estimation Partition Regional/Spatial Science Reliability Sampling Sampling methods Social media social media data Social networks Spatial data Travel Travel demand Trip generation Urban planning Workplaces
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Summary:	Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of geographical partition. However, the data also have significant limitations: population and behavioural biases, and lack of important information such as trip purpose and social demographics. This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size. We show that Twitter data are suitable for modelling the overall travel demand for an average weekday but not for commuting travel demand, due to the low reliability of identifying home and workplace. Collecting more detailed, long-term individual data from user timelines for a small number of individuals produces more accurate results than short-term data for a much larger population within a region. We developed a novel approach using geotagged tweets as attraction generators as opposed to the commonly adopted trip generators. This significantly increases usable data, resulting in better representation of travel demand. This study demonstrates that Twitter can be a viable option for estimating travel demand, though careful consideration must be given to sampling method, estimation model, and sample size.
ISSN:	0049-4488 1572-9435 1572-9435