Zajac, Martin;
Horak, Jiri;
Osorio-Arjona, Joaquin;
Kukuliac, Pavel;
Haworth, James;
(2022)
Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period-Temporal and Spatial Differences in Activity Topics.
Sustainability
, 14
(24)
, Article 17055. 10.3390/su142417055.
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Abstract
Public transport requires constant feedback to improve and satisfy daily users. Twitter offers monitoring of user messages, discussion and emoticons addressed to official transport provider accounts. This information can be particularly useful in delicate situations such as management of transit operations during the COVID-19 pandemic. The behaviour of Twitter users in Madrid, London and Prague is analysed with the goal of recognising similar patterns and detecting differences in traffic related topics and temporal cycles. Topics in transit tweets were identified using the bag of words approach and pre-processing in R. COVID-19 is a dominant topic for both London and Madrid but a minor one for Prague, where Twitter serves mainly to deliver messages from politicians and stakeholders. COVID-19 interferes with the meaning of other topics, such as overcrowding or staff. Additionally, specific topics were discovered, such as air quality in Victoria Station, London, or racism in Madrid. For all cities, transit-related tweeting activity declines over weekends. However, London shows much less decline than Prague or Madrid. Weekday daily rhythms show major tweeting activity during the morning in all cities but with different start times. The spatial distribution of tweets for the busiest stations shows that the best-balanced tweeting activity is found in Madrid metro stations.
| Type: | Article |
|---|---|
| Title: | Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period-Temporal and Spatial Differences in Activity Topics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/su142417055 |
| Publisher version: | https://doi.org/10.3390/su142417055 |
| Language: | English |
| Additional information: | © 2022 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Twitter; public transport; COVID-19; time analysis; text mining |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164186 |
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