Within-day travel speed pattern unsupervised classification – A data driven case study of the State of Alabama during the COVID-19 pandemic

Abstract

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 time-dependent 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.

Keywords

• COVID-19
• Within-day traffic dynamics
• Dynamic time warping
• Hierarchical clustering
• Unsupervised classification