Accident; analysis and prevention
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Taxi drivers worldwide often have very long driving hours and experience frequent fatigue. These conditions are associated with a high prevalence of fatigue and accidents. However, the key factors that distinguish high/low fatigue-related accident risk (FRAR) taxi drivers are uncertain. ⋯ The FRAR model with only four major measurable predictors achieved a sensitivity of 91.9% and a specificity of 94.6% on predicting labeled data. Adjusting drive-rest habits and self-evaluation pertaining to these predictors is good for high-risk drivers to mitigate their accident risk. It was concluded that taxi drivers' drive-rest habits, experience, and intention for fatigue driving are crucial, and to a large degree determine their FRAR, and the prediction model can satisfactorily identify high-risk taxi drivers.
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This study aims to investigate pedestrian crossing behavior and safety at uncontrolled mid-block crosswalks with different numbers of vehicle lanes. For this purpose, twelve uncontrolled mid-block crosswalks in Wuhan, China were selected to collect data via field investigation. Descriptive statistics were used to analyze pedestrian crossing behavior, and the distribution of pedestrian-vehicle conflicts on different vehicle lanes was given. ⋯ As the number of vehicle lanes increases, the proportion of pedestrians adopting the rolling gap crossing mode, crossing the street with others, and changing the speed or path increase accordingly. Moreover, the number of pedestrian-vehicle conflicts at two-way six-lane crosswalks is 5.96 times higher than that of two-lane crosswalks, and 2.04 times higher than that of four-lane crosswalks. From the results of OP models, it was found that pedestrian behavioral characteristics such as rolling gap crossing mode, crossing with others significantly increased the possibility of pedestrian-vehicle conflicts.