Feasibility of estimating travel demand using geolocations of social media data
Journal article, 2022

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.

gravity model

social media data

lateral data

origin-destination estimation

travel demand

longitudinal data


Yuan Liao

Chalmers, Space, Earth and Environment, Physical Resource Theory

Sonia Yeh

Chalmers, Space, Earth and Environment, Physical Resource Theory

Jorge Gil

Chalmers, Architecture and Civil Engineering, Urban Design and Planning


0049-4488 (ISSN) 1572-9435 (eISSN)

Vol. 49 1 137-161

Sustainable cities: the use of large amounts of data to understand and handle movement patterns and congestion

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

Areas of Advance

Information and Communication Technology


Driving Forces

Sustainable development

Subject Categories

Civil Engineering

Transport Systems and Logistics



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4/5/2022 5