Modeling and mitigating fatigue-related accident risk of taxi drivers.

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. By examining a series of potential factors related with fatigue or accident risk as discussed in previous research, the objective was to find out the most important factors that relate to taxi driver's FRAR, and to investigate the association of these factors and taxi driver's FRAR. Modeling methods were applied to questionnaire data collected from Beijing taxi drivers. A 269-sample dataset was analyzed to identify key factors related to FRAR and to fit FRAR prediction models. The model's performance on high-risk driver prediction was then tested using another independently collected 100-sample dataset. High-risk taxi drivers had significantly longer driving hours per working day, lower rest ratios, less driving experience, and were more confident about their fatigue resistance. 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.

An investigation of detection biases in the unattended periphery during simulated driving.

While people often think they veridically perceive much of the visual surround, recent findings indicate that when asked to detect targets such as gratings embedded in visual noise, observers make more false alarms in the unattended periphery. Do these results from psychophysics studies generalize to more ecologically valid settings? We used a modern game engine to create a simulated driving environment where participants (as drivers) had to make judgments about the colors of pedestrians' clothing in the periphery. Confirming our hypothesis based on previous psychophysics studies, we found that subjects showed liberal biases for unattended locations when detecting specific colors of pedestrians' clothing. A second experiment showed that this finding was not simply due to a confirmation bias in decision-making when subjects were uncertain. Together, these results support the idea that in everyday visual experience, there is subjective inflation of experienced detail in the periphery, which may happen at the decisional level.