Feasibility of estimating travel demand using geolocations of social media data
Artikel i vetenskaplig tidskrift, 2021

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

Författare

Yuan Liao

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

Sonia Yeh

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

Jorge Gil

Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad

Transportation

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

Vol. In Press

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

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

Styrkeområden

Informations- och kommunikationsteknik

Transport

Drivkrafter

Hållbar utveckling

Ämneskategorier

Samhällsbyggnadsteknik

Transportteknik och logistik

DOI

10.1007/s11116-021-10171-x

Mer information

Senast uppdaterat

2021-04-16