Discovering research topics, trends, and perspectives in COVID-19-related transportation journal articles

Abstract Research interest in COVID-19 in the transportation field has increased sporadically since its outbreak in 2019. This has led to an unprecedented increase in the number of publications in academic journals, rendering it difficult to clearly capture and understand the themes being discussed in the entire literature. This study employs a Structural Topic Model, a robust probabilistic topic model that incorporates document-level metadata to extract hidden topics in unstructured textual big data that focuses on COVID-19 and transportation. To understand the topics identified, the study examined the topical trends over time and compared them to provide insights into authors’ perspectives based on their country’s economic status. In total, abstracts from 421 research articles published in top transportation/transportation science journals were collected and analysed. The results reveal that the major academic concerns in the area of COVID-19 and transportation are related to the changing travel behaviour, airport financial performance, and supply chain optimisation. Overall, research trends seem to be shifting towards shipping emissions, air transport recovery, travel behaviour, and the performance of airports. In addition, authors from both high-income and middle-and low-income countries were found to have different perspectives regarding the topics identified. The findings from this study contribute to understanding topical trends and perspectives in the literature on COVID-19 and transportation and can be used by researchers, policymakers, and fund providers to recognise current research issues to guide future research direction and for making more informed policy decisions.