Review of United States research and guidelines on left turn lane offset:Unsignalized intersections and signalized intersections with permitted left turns

The purposes of this paper are to better understand the design and impact of left turn lane offset and provide state transportation agencies recommendations on best practices and alternative design options to address left turn sight line obstruction issues.Research studies and existing guidelines on left turn lane offset were reviewed and summarized to offer insights in driver sight distance and behavior,intersection safety and operations,as well as design elements related to left turn lane offset.Studies showed that at both unsignalized intersections and signalized intersections with permitted left turns,obstructed sight line could cause higher possibilities of collisions between left turning vehicles and oncoming vehicles from the opposing direction.Existing evaluations of left turn lane offset,with data from multiple states in the United States,reported that positive left turn lane offsets were beneficial in terms of safety and operations.Most agency guidelines provided very limited discussions about left turn lane offset.This review demonstrated that positive left turn lane offsets are beneficial to intersection safety and operations.Based on the review of research and guidelines,recommendations were provided for transportation agencies for left turn lane offset implementation.A discussion on new left turn design concepts was also included to point out directions for future research and practice.

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

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.