{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:32:03Z","timestamp":1788949923885,"version":"build-2803163510"},"reference-count":56,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Translink New Mobility Grant"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Environment and Planning B: Urban Analytics and City Science"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Using open-source data, we show that despite significant reductions in global public transit during the COVID-19 pandemic, \u223c20% of ridership continues during social distancing measures. Current urban transport data collection methods do not account for the distinct behavioural and psychological experiences of the population. Therefore, little is known about the travel experience of vulnerable citizens that continue to rely on public transit and their concerns over risk, safety and other stressors that could negatively affect their health and well-being. We develop a machine learning approach to augment conventional transport data collection methods by curating a population segmented Twitter dataset representing the travel experiences of \u223c120,000 transit riders before and during the pandemic in Metro Vancouver, Canada. Results show a heightened increase in negative sentiments, differentiated by age, gender and ethnicity associated with public transit indicating signs of psychological stress among travellers during the first and second waves of COVID-19. Our results provide empirical evidence of existing inequalities and additional risks faced by citizens using public transit during the pandemic, and can help raise awareness of the differential risks faced by travellers. Our data collection methods can help inform more targeted social-distancing measures, public health announcements, and transit monitoring services during times of transport disruptions and closures.<\/jats:p>","DOI":"10.1177\/23998083221104489","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T09:56:26Z","timestamp":1654077386000},"page":"60-75","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["Monitoring the well-being of vulnerable transit riders using machine learning based sentiment analysis and social media: Lessons from COVID-19"],"prefix":"10.1177","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8947-3829","authenticated-orcid":false,"given":"Martino","family":"Tran","sequence":"first","affiliation":[{"name":"School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Draeger","sequence":"additional","affiliation":[{"name":"Department of Earth, Ocean and Atmospheric Sciences, Faculty of Science, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuerou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbas","family":"Nikbakht","sequence":"additional","affiliation":[{"name":"School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"bibr1-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0168893"},{"key":"bibr2-23998083221104489","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2015905866"},{"key":"bibr3-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.06.031"},{"key":"bibr4-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59576-1"},{"key":"bibr5-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102846"},{"key":"bibr6-23998083221104489","first-page":"1","volume":"0","author":"Brooks JHM","year":"2020","journal-title":"British Journal of Sports Medicine"},{"key":"bibr7-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1126\/science.abb8021"},{"key":"bibr8-23998083221104489","doi-asserted-by":"publisher","DOI":"10.1016\/j.trip.2020.100151"},{"key":"bibr9-23998083221104489","unstructured":"Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. 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