Sep 2026· Accident Analysis and Prevention· Vol 238, pp.
108751
· 1 citation· 56 references
Medicine
Abstract
Roadside trees represent the most frequently struck fixed object in fatal crashes in the United States and constitute a substantial safety concern, particularly on high-speed rural roadways. Although trees are often retained or planted along roadways for aesthetic and environmental benefits, their presence within the roadside environment can significantly increase the risk of severe injury outcomes. This study examines the factors influencing driver injury severity in vehicle-tree collisions using a five-year dataset (2020-2024) from the state of Alabama. Following data cleaning and validation, 17,555 crash records were analyzed. Descriptive analysis indicated that fatal and serious injury crashes accounted for 15.20% of cases, with 73.14% occurring in rural areas, and 10.50% involving driving under the influence. To better understand the determinants of injury severity, both traditional regression models and advanced machine learning techniques were employed. The results consistently identified seat belt use as the most influential factor, followed by driver condition (impaired versus non-impaired) and driver gender. Additional significant predictors included crash location, weather conditions, and vehicle type. While these factors have been associated with injury severity in broader crash contexts, their prominence in vehicle-tree collisions reinforces the importance of established safety interventions. From a policy perspective, the findings point to restraint use, impaired driving, and the rural high-speed context as the associations most consistently identified, and are discussed alongside existing roadside design guidance on clear-zone treatment and barrier protection, to mitigate the severity of these high-risk crash events.
Road traffic accidents continue to be a leading public safety concern in urban areas, and particularly for vulnerable road users including pedestrians and motorcyclists. While the increasing fatality and casualty counts urge a comprehensive approach to understand the factors contributing to crash risks, only a few stud...
Trupti Narkhede, L. Gupta, Prasun Chakrabarti et al.· International Journal of Civ...· 0 citations
Introduction Truck-involved fatal crashes on rural roadways often result in severe safety consequences for truck occupants, and the effects of contributing factors may vary across lighting conditions. This study aimed to examine truck occupant injury severity under daylight, dark-not-lighted, and dark-lighted condition...
Yalong Yuan, Shuwei Zhang· Frontiers in Public Health· 0 citations
As U.S. cities expand multimodal transportation networks and pursue Vision Zero goals, crash severity among pedestrians and bicyclists remains a critical public safety challenge. Charlotte, NC, offers a timely case study given its rapid urban growth, rising active transportation demand, and commitment to Vision Zero. T...
Fatemeh Abdous, Ramina Javid, Celeste Chavis et al.· Frontiers in Sustainable Cit...· 0 citations
OBJECTIVE
Head-on crashes on undivided roads are particularly dangerous, often occurring at high impact speeds that may lead to fatal injury. This study examines the relationship between changes in vehicle velocity (delta-V) and fatality risk for head-on passenger vehicle crashes, which may assist regulators in setting...
Kardina Ayuningtyas, R. Grzebieta, C. Caponecchia et al.· Traffic Injury Prevention· 0 citations
Roadside type is a crucial factor that affects road safety. This study aims to investigate the impact of 15 roadside types using the international Road Assessment Program (iRAP) model, which utilises the star rating system as a measure of road safety. The study, uniquely, focuses on using risk assessment models to eval...
Atheer Al-nuaimi, S. Alsadik, H. Evdorides· Journal of Engineering and S...· 0 citations
Objectives: This study examines how demographic, temporal, vehicular, and spatial conditions jointly influence severe road traffic crashes in a dense metropolitan road network and evaluates whether machine learning can support policy-relevant risk screening. Methods/Analysis: Police-based crash records from 2020-2024 w...
M. Worachairungreung, N. Kulpanich, P. Sae-ngow et al.· HighTech and Innovation Jour...· 0 citations
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