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Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017-2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65-74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.
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http://dx.doi.org/10.1016/j.aap.2024.107815 | DOI Listing |
Injury
August 2025
University of Seoul, Department of Transportation, College of Urban Sciences, Seoul, South Korea. Electronic address:
Pedestrian crashes are a global safety issue impacting all age groups, and despite extensive research, understanding the severity of crashes among different age groups has remained incomplete. Older and young pedestrians represent two distinct demographics with unique vulnerabilities. This paper examines the factors that impact the severity of pedestrian crashes resulting in Killed or Serious Injuries in South Australia over ten years (2012-2020) for two age groups, namely young pedestrians (age < 18) and older pedestrians (age > 65).
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Urban Construction and Transportation, Hefei University, Hefei 230606, China.
This study focuses on exploring the differences in driving abilities in emergency traffic situations between older drivers (aged 60-70) and young drivers (aged 20-35) in a simple traffic environment. Two typical emergency scenarios were designed in the experiment: Scenario A (intrusion of electric bicycles) and Scenario B (pedestrians crossing the road). The experiment employed a driving simulation system to synchronously collect data on eye movement characteristics, driving behavior, and physiological metrics from 30 drivers.
View Article and Find Full Text PDFJ Clin Med
August 2025
Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA.
Traumatic brain injuries (TBI) account for over a third of all injury-related deaths, predominantly due to motor vehicle collisions (MVC). This study provides a comprehensive analysis of TBI trends in Eastern Massachusetts, focusing on injuries resulting from motorcycle MVCs (mMVC), non-motorcycle MVCs (nmMVC), and pedestrian-vehicle strikes (PVS). A retrospective analysis was conducted on TBI patients admitted between 2010 and 2020 to Boston Medical Center.
View Article and Find Full Text PDFSwiss Med Wkly
June 2025
Swiss Trauma Board.
Background: Information on severely injured patients transported by helicopter emergency medical services (HEMS) in Switzerland is scarce. This study, with a special focus on sex differences, aimed to gain insights into the demographics, injury characteristics and outcomes of these patients and to provide data that could help improve prehospital trauma care.
Methods: This is a retrospective multicentre cohort study analysing data collected by the Swiss Trauma Registry.
Sci Rep
July 2025
Architecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology, Maoming, 525000, China.
Global population aging highlights the need to understand how the elderly perceive safety in urban public spaces. This study used image semantic segmentation to identify key visual elements from panoramic images. A dataset was created by combining manual scoring with deep learning to explore how pocket park environments impact older adults' safety perceptions.
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