Recently, there has been a large increase in fatal pedestrian crashes that occur at night in the United States of America. In the state of Michigan, for the past 11 years (2011–2021), 76% of all fatal pedestrian crashes occurred at night. With the Toward Zero Death national strategy, identifying the presence of disparities by population characteristics in pedestrian nighttime crashes will enable better allocation of resources to the overrepresented segments of the population and enable the implementation of focused countermeasures to improve the safety of everyone. This study, therefore, aims to investigate the racial and poverty status disparities associated with pedestrian nighttime crashes in Michigan. Pedestrian nighttime crashes from 2011 to 2021 were aggregated at a census tract level. Demographic, socioeconomic, and built environment data were included at a census tract level. The negative binomial model was used to identify whether the racial makeup and economic status of census tracts are significantly associated with the increase in pedestrian nighttime crashes. Results show that census tracts where the majority of people are Black are associated with a 20% increase in pedestrian nighttime crashes. Moreover, poor tracts are associated with a 40% increase in pedestrian nighttime crashes. It was revealed that crashes recorded by police officers as “dark unlighted” are concentrated more in poor census tracks. Also, dark unlighted crashes were more concentrated in a census tract where a majority of the residents identify as Black compared with the majority of residents who identify as White. The use of LED lighting is recommended to improve pedestrian visibility at night.


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    Titel :

    Investigating Racial and Poverty-Level Disparities Associated with Pedestrian Nighttime Crashes


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:


    Erscheinungsdatum :

    03.03.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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