Augmenting Ridership Data with Social Media Data to Analyse the Long-term Effect of COVID-19 on Public Transport

Augmenting Ridership Data with Social Media Data to Analyse the Long-term Effect of COVID-19 on Public Transport

Xu, Y. (TU Delft Transport and Planning)
Krishnakumari, P.K. (TU Delft Transport and Planning)
Yorke-Smith, N. (TU Delft Algorithmics)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Transport and Planning

2023

COVID-19 significantly influenced travel behaviours and public attitudes towards public transport. Various studies have illustrated complicated factors related to long-term travel behaviour, indicating difficulty in understanding and predicting post-pandemic long-term travel behaviour via traditional methods. In these complex circumstances, it is valuable to take advantage of social media data to obtain real-time public opinions to understand dynamic travel behaviour changes from the passenger perspective. The present study provides a means - leveraging Twitter data and state-of-art Natural Language Processing (NLP) technologies - to interpret the underlying associations among public attitude, COVID-19 trends and public travel behaviour. Concretely, New York City has been selected due to its dependence on public transit for daily commuting. More than 500K tweets have been collected from January 2019 to June 2022. Automated text mining, topic modelling, and sentiment analysis have been implemented in these contexts to identify dynamic public reactions. A consistently negative attitude to public transit is detected and five main topics, including derivative topics from COVID-19, are discovered within the COVID-19 duration. Policy makers and transit managers can use these topics to take onboard the public's concerns. The paper thus exemplifies how social media data and NLP technologies can support policy-making progress and can benefit other tasks in the transportation domain.

COVID-19
natural language processing
public transport travel behaviour
sentiment analysis
social media
topic modelling

http://resolver.tudelft.nl/uuid:13e2456b-b643-4a1f-86d3-86fd47db701d

https://doi.org/10.1109/MT-ITS56129.2023.10241480

Institute of Electrical and Electronics Engineers (IEEE)

2023-12-16

9781665455305

2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2023

8th International Conference on Models and Technologies for Intelligent Transportation Systems, 2023-06-14 → 2023-06-16, Nice, France

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Y. Xu, P.K. Krishnakumari, N. Yorke-Smith, S.P. Hoogendoorn