Road network screening is traditionally based on analyzing historical crash data to identify road sections (hotspots) that could benefit from safety improvement. However, crash data analysis for road safety improvement is inherently reactive, given that crash risks are estimated in hindsight. This study examines the po...
S. Okafor, Steven Jones, Cailis Bullard et al.· Accident Analysis and Preven...· 0 citations
Results show that older cyclist age, higher vehicle speeds, intersection locations, multi-lane roadways, and dark conditions are associated with higher probabilities of evident or more severe injuries, highlighting the predictive capability of spatial machine learning while retaining interpretable geographic variation,...
Yan-Fang Su, Zihe Zhang, Jun Liu et al.· Future Transportation· 0 citations
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 r...
E. Adanu, Yan-Fang Su, S. Khan et al.· Accident Analysis and Preven...· 1 citation
This study proposes a comprehensive framework for driving volatility analysis at both the segment and intersection levels using large-scale connected vehicle trajectory data, revealing a strong and statistically significant relationship between elevated driving volatility and higher crash frequencies.
Xiaoqin Zou, Jiayi Kong, Zihe Zhang et al.· Journal of Intelligent Trans...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
