Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic ...Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic burdens.Therefore,this significant safety threat requires a thorough investigation.To address the temporal instability of factors contributing to crashes involving drowsy drivers,this paper divides the crash data into four time periods while capturing unobserved heterogeneity in the means and variances of random parameters.To explore the determinants affecting the severity of injuries sustained by drowsy drivers involved in single-vehicle crashes,injury outcomes are categorized into three groups:serious,moderate,and no injuries.Using four years of crash data from the state of Washington between 2013 and 2016,a wide range of factors were examined,including driver characteristics,roadway conditions,crash characteristics,vehicle conditions,lighting conditions,and temporal factors.The estimation results reveal that there is temporal instability in terms of the effect of determinants on injury severity across the years.However,some factors exhibit stable effects,such as female drivers,sober drivers,and non-hit-and-run crashes.Based on the findings of this study,decision-makers,traffic engineers,and traffic authorities can gain valuable knowledge and insights into the factors contributing to drowsy-related crashes,enabling them to make informed recommendations for safety countermeasures.展开更多
Pedestrians are the most at-risk group in the transportation system,experiencing a trou-bling and persistent rise in both the frequency and proportion of fatalities over the past decade.In 2022,pedestrian fatalities i...Pedestrians are the most at-risk group in the transportation system,experiencing a trou-bling and persistent rise in both the frequency and proportion of fatalities over the past decade.In 2022,pedestrian fatalities in the United States reached a record high since 1981.Despite extensive research examining crash severity models from various perspec-tives,there is a notable paucity of studies investigating the impact of seasonal variations.This study addresses this gap by examining the seasonal variation in potential contributing factors to pedestrian-vehicle crash severity,utilizing nine years of the most recent pedes-trian crash data from North Carolina.Preliminary analysis shows higher crash frequencies during darker seasons,with the highest frequency observed in the fall.Seasonally tempo-rally constrained random parameter logit models,incorporating heterogeneity in both means and variances,are employed to explain the unobserved heterogeneity inherent in the variables and to mitigate biased parameter estimation.The necessity for segmentation is corroborated through pairs of likelihood ratio tests.The model outcomes reveal signifi-cant seasonal variations in several important variables.For instance,hit-and-run incidents exhibit the highest marginal effects in the spring.During the summer,male drivers,week-ends,and alcohol impairment for both drivers and pedestrians have the most substantial marginal effects.In the fall,young drivers(aged under 24),darkness irrespective of light-ing,and work zones show the highest magnitude of marginal effects.This study offers a novel perspective on pedestrian safety analysis and provides data-driven recommenda-tions to enhance safety planning,resource allocation,and overall improvements in the transportation system.展开更多
文摘Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic burdens.Therefore,this significant safety threat requires a thorough investigation.To address the temporal instability of factors contributing to crashes involving drowsy drivers,this paper divides the crash data into four time periods while capturing unobserved heterogeneity in the means and variances of random parameters.To explore the determinants affecting the severity of injuries sustained by drowsy drivers involved in single-vehicle crashes,injury outcomes are categorized into three groups:serious,moderate,and no injuries.Using four years of crash data from the state of Washington between 2013 and 2016,a wide range of factors were examined,including driver characteristics,roadway conditions,crash characteristics,vehicle conditions,lighting conditions,and temporal factors.The estimation results reveal that there is temporal instability in terms of the effect of determinants on injury severity across the years.However,some factors exhibit stable effects,such as female drivers,sober drivers,and non-hit-and-run crashes.Based on the findings of this study,decision-makers,traffic engineers,and traffic authorities can gain valuable knowledge and insights into the factors contributing to drowsy-related crashes,enabling them to make informed recommendations for safety countermeasures.
基金support by the United States Department of Transportation,University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education(CAMMSE)at The University of North Carolina at Charlotte(No.69A3551747133).
文摘Pedestrians are the most at-risk group in the transportation system,experiencing a trou-bling and persistent rise in both the frequency and proportion of fatalities over the past decade.In 2022,pedestrian fatalities in the United States reached a record high since 1981.Despite extensive research examining crash severity models from various perspec-tives,there is a notable paucity of studies investigating the impact of seasonal variations.This study addresses this gap by examining the seasonal variation in potential contributing factors to pedestrian-vehicle crash severity,utilizing nine years of the most recent pedes-trian crash data from North Carolina.Preliminary analysis shows higher crash frequencies during darker seasons,with the highest frequency observed in the fall.Seasonally tempo-rally constrained random parameter logit models,incorporating heterogeneity in both means and variances,are employed to explain the unobserved heterogeneity inherent in the variables and to mitigate biased parameter estimation.The necessity for segmentation is corroborated through pairs of likelihood ratio tests.The model outcomes reveal signifi-cant seasonal variations in several important variables.For instance,hit-and-run incidents exhibit the highest marginal effects in the spring.During the summer,male drivers,week-ends,and alcohol impairment for both drivers and pedestrians have the most substantial marginal effects.In the fall,young drivers(aged under 24),darkness irrespective of light-ing,and work zones show the highest magnitude of marginal effects.This study offers a novel perspective on pedestrian safety analysis and provides data-driven recommenda-tions to enhance safety planning,resource allocation,and overall improvements in the transportation system.
