Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicato...Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders,which however may not accurately present the actual impacted dates.The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns.Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order,the methodology in this study investigates the within-day timedependent travel speed as time series,and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group.Using the state-wide travel speed data in Alabama,these study measures dissimilarities among within-day travel speed time series.By incorporating the dissimilarities/distance matrix,various agglomerative hierarchical clustering(AHC)methods(average,complete,Ward’s)are tested to conduct proper unsupervised classification.The Ward’s AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order.The results further show that a new travel speed pattern appears at the end of stay-at-home order,which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern,which leads to a conclusion that a’new normal’within-day travel pattern emerges.展开更多
基金supported by New Faculty Award from UAH’s Office of the Vice President for Research and Economic Development.
文摘Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders,which however may not accurately present the actual impacted dates.The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns.Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order,the methodology in this study investigates the within-day timedependent travel speed as time series,and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group.Using the state-wide travel speed data in Alabama,these study measures dissimilarities among within-day travel speed time series.By incorporating the dissimilarities/distance matrix,various agglomerative hierarchical clustering(AHC)methods(average,complete,Ward’s)are tested to conduct proper unsupervised classification.The Ward’s AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order.The results further show that a new travel speed pattern appears at the end of stay-at-home order,which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern,which leads to a conclusion that a’new normal’within-day travel pattern emerges.
