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
Road traffic crashes (RTCs) remain a major public safety challenge in Ghana, with fatality rates in regions such as the Central Region continuing to rise despite ongoing road safety interventions, including infrastructure upgrades and public education campaigns. This persistence suggests that long-term patterns and tem...
William Kwaasi Amanor, E. Adanu, Richard Dzinyela et al.· Discover Civil Engineering· 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
The findings show that a single controller trained with surrogate safety indicators as learning objectives can improve operational performance while reducing safety-critical instability in work zones.
Israel Afriyie, Kwadwo Amankwah-Nkyi, Percy Agyei-Essiful et al.· Future Transportation· 1 citation
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