Tran Martino, Draeger Christina, Wang Xuerou, Nikbakht Abbas
School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, Vancouver, BC, Canada.
Department of Earth, Ocean and Atmospheric Sciences, Faculty of Science, University of British Columbia, Vancouver, BC, Canada.
Environ Plan B Urban Anal City Sci. 2023 Jan;50(1):60-75. doi: 10.1177/23998083221104489.
Using open-source data, we show that despite significant reductions in global public transit during the COVID-19 pandemic, ∼20% 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 ∼120,000 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.
利用开源数据，我们发现，尽管在新冠疫情期间全球公共交通客流量大幅下降，但在社交距离措施实施期间仍有大约20%的客流量。当前的城市交通数据收集方法并未考虑到人群不同的行为和心理体验。因此，对于那些继续依赖公共交通的弱势群体的出行体验，以及他们对可能对其健康和福祉产生负面影响的风险、安全和其他压力源的担忧，我们知之甚少。我们开发了一种机器学习方法，通过整理一个按人群细分的推特数据集来扩充传统的交通数据收集方法，该数据集代表了加拿大温哥华地铁地区约12万名公交乘客在疫情之前及期间的出行体验。结果显示，与公共交通相关的负面情绪显著增加，且因年龄、性别和种族而异，这表明在新冠疫情的第一波和第二波期间，乘客中存在心理压力迹象。我们的结果为疫情期间使用公共交通的市民所面临的现有不平等和额外风险提供了实证证据，并有助于提高人们对乘客所面临的不同风险的认识。我们的数据收集方法有助于在交通中断和停运期间，为更具针对性的社交距离措施、公共卫生公告及交通监测服务提供参考。
