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.
E. Adanu, Yan-Fang Su, S. Khan et al.· Accident Analysis and Preven...· 0 citations
Abstract The advancement of Intelligent Transportation Systems (ITS) and the proliferation of Connected and Automated Vehicles (CAVs) have enabled the collection of large-scale, high-resolution vehicle movement data through crowdsourcing. This wealth of data presents a unique opportunity to move beyond traditional, retrospective crash-based analyses toward more proactive and dynamic approaches to traffic safety assessment and management. This study proposes a comprehensive framework for driving volatility analysis at both the segment and intersection levels using large-scale connected vehicle trajectory data. Driving volatility—characterized by abrupt changes in acceleration, deceleration, and speed—is used as a behavioral safety surrogate to identify locations with elevated crash risk before crashes occur. The analysis integrates volatility metrics with roadway geometry, traffic volume, and historical crash data to develop predictive models of crash frequency. Results reveal a strong and statistically significant relationship between elevated driving volatility and higher crash frequencies, with segment-level volatility demonstrating particularly robust predictive power. These findings highlight the potential of incorporating driving volatility into ITS-based safety monitoring systems, enabling real-time detection of high-risk locations and more targeted, data-driven safety interventions. By leveraging CAVs as mobile sensors within an ITS framework, transportation agencies can enhance situational awareness, support proactive traffic safety management strategies, and improve roadway safety outcomes. This approach represents a significant shift toward real-time, behavior-based safety diagnostics in the era of intelligent and connected transportation systems.
Xiaoqin Zou, Jiayi Kong, Zihe Zhang et al.· Journal of Intelligent Trans...· 0 citations
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