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
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.
