{"title":"Unsupervised origin-destination flow estimation for analyzing COVID-19 impact on public transport mobility","authors":"Lan Zhang, Kaijian Liu","doi":"10.1016/j.cities.2024.105086","DOIUrl":null,"url":null,"abstract":"<div><p>The outbreak of COVID-19 caused unprecedented disruptions to public transport services. As such, this paper proposes a methodology for analyzing COVID-19 impact on public transport mobility. The proposed methodology includes: (1) a new unsupervised machine learning (UML) method, which utilizes a decoder-encoder architecture and a flow property-based learning objective function, to estimate the origin-destination (OD) flows of public transport systems from boarding-alighting data; and (2) a temporal-spatial analysis method to analyze OD flow change before and during COVID-19 to unveil its impact on mobility across time and space. The validation of the UML method showed that it achieved a coefficient of determination of 0.836 when estimating OD flows using boarding-alighting data. Upon the successful validation, the proposed methodology was implemented to analyze the impact of COVID-19 on the mobility of the New York City subway system. The implementation results indicate that (1) the rise in the number of weekly new COVID-19 cases intensified the impact on the public transport mobility, but not as strongly as public health interventions; and (2) the inflows to and outflows from the center of the city were more sensitive to the impact of COVID-19.</p></div>","PeriodicalId":48405,"journal":{"name":"Cities","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cities","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264275124003007","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"URBAN STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
The outbreak of COVID-19 caused unprecedented disruptions to public transport services. As such, this paper proposes a methodology for analyzing COVID-19 impact on public transport mobility. The proposed methodology includes: (1) a new unsupervised machine learning (UML) method, which utilizes a decoder-encoder architecture and a flow property-based learning objective function, to estimate the origin-destination (OD) flows of public transport systems from boarding-alighting data; and (2) a temporal-spatial analysis method to analyze OD flow change before and during COVID-19 to unveil its impact on mobility across time and space. The validation of the UML method showed that it achieved a coefficient of determination of 0.836 when estimating OD flows using boarding-alighting data. Upon the successful validation, the proposed methodology was implemented to analyze the impact of COVID-19 on the mobility of the New York City subway system. The implementation results indicate that (1) the rise in the number of weekly new COVID-19 cases intensified the impact on the public transport mobility, but not as strongly as public health interventions; and (2) the inflows to and outflows from the center of the city were more sensitive to the impact of COVID-19.
期刊介绍:
Cities offers a comprehensive range of articles on all aspects of urban policy. It provides an international and interdisciplinary platform for the exchange of ideas and information between urban planners and policy makers from national and local government, non-government organizations, academia and consultancy. The primary aims of the journal are to analyse and assess past and present urban development and management as a reflection of effective, ineffective and non-existent planning policies; and the promotion of the implementation of appropriate urban policies in both the developed and the developing world.