نتایج جستجو برای: accident-prone segments
تعداد نتایج: 240325 فیلتر نتایج به سال:
One of the most important and effective factors in prioritizing high accident prone segments of roads for pedestrians is accident cause which up to now has been neglected in current methods. Since accidents are not additives due to various reasons, prioritizing these segments without considering the accident cause makes the selection of high accident prone segments inaccurate. In this paper, by...
During last decades, owing to the increase in a number of vehicles, the rate of accident occurrence grows significantly. Efforts must be made to provide efficient tools to prioritize segments requiring safety improvement and identify influential factors on accidents. This objective of the research was to determine the safety oriented threshold of International Roughness Index (IRI) to recognize...
Big data analytics for traffic accidents is a hot topic and has significant values for a smart and safe traffic in the city. Based on the massive traffic accident data from October 2014 to March 2015 in Xiamen, China, we propose a novel accident occurrences analytics method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur conseq...
Introduction: Investigations show that the scattering of accidents in individuals is not the same, and three quarters of all accident occur to one quarter of accident prone individuals. The aim of the study was to design and validate a questionnaire for screening out the accident prone individuals. Methods: This study in 5 stages of item generation, content validity assessment, reliability, ...
AbstractObjectives: This study has been carried out with the aim of investigating motor, cognitive and executive functions of a group of accident-prone drivers. Method: Seventy professional accident-prone drivers with major faults and 30 drivers recognized as good and safe drivers, were examined using tests that assessed reaction time, recognition, memory, attention and concentration, as well a...
Background and Aim: The aim of this study was to predict driving error, lapses and violations from five narrow-band personality traits of Eyseneck Personality Profile, Vienna Risk Taking test-Traffic and Considering Future Consequences Scale. Methods: Current study is a relational study. 510 drivers Was selected randomly and divided to two groups accident namely prone(n=257) and non acciden...
Safely avoiding accident prone routes is vital to maintaining safe and intelligent transportation. Real-time crowd sourcing of accident causing factors (e.g., icy roads, rain slicked road patches) combined with historical accident information can be utilized by intelligent navigation systems to avoid accident prone routes. A vehicle cloud can compute such safe routes and react faster than a cen...
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