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The bikeshare program in Taipei City and New Taipei City, called U-bike, was launched in August 2012 and has more than 7500 bicycles operating out of 769 stations. Research has suggested that bicycle helmet use is a means of reducing morbidity and mortality among bike users. Helmets, however, are not available for rent when a U-bike is rented. The current research conducted an observational study to examine the prevalence of helmet non-use by users of the bikeshare program, electric bicycles, racing bicycles, and personal bicycles in Taipei City and New Taipei City. Trained observers using compact video cameras collected helmet non-use data during various times of the day and on different days of the week. Observers collected data on cyclist attributes, bicycle types, and helmet use at several selected locations within Taipei City and New Taipei City. U-bike users were found to be the least likely to wear helmets. Other noteworthy findings include that violations such as phone use, red-light violations, and travelling at ≥25 km/h were associated with riding without a helmet. Male users of racing bikes tended not to wear helmets, while female users of other bicycle types were less likely to use a helmet. Carrying passengers by users of electric bikes and personal bikes was a determinant of helmet non-use. This paper concludes with a discussion and recommendations for future research. [ABSTRACT FROM AUTHOR]

A dynamic visual information processing task was designed to investigate time-based and intensity-based factors on an operator's information processing load as measured by reaction time, pupil diameter, and eye movement parameters. The time-based factor was manipulated by the target rate and scanning rate while the intensity-based factor was manipulated by the difference between a simple reaction task and a physical matching (choice reaction) task. Nine participants tracked the scanning line at two different scanning rates and were required to respond to two designated targets presented singly at two different temporal frequencies. The results indicated that task difficulty (the intensity-based factor) had a significant effect on the reaction time. Target rate and scanning rate were integrated as one time-based factor in terms of three sweeping angles. The time-based factor was found to have a significant effect on the fixation time, saccade amplitude, fixation frequency, eye movement speed, reaction time and hit rate. No interaction effect was found between time-based and intensity-based factors. The time pressure (defined by the time required divided by the time available) based on a model human processor was positively related to scanning rate, target rate and task difficulty. It was found to be the most objective and reliable if time required can be reliably predicted based on a predictive model approach. [ABSTRACT FROM AUTHOR]

As inspection moves from unaided human skills to human-computer hybrid tasks, there is a need for models of the human and the computer which have common parameters. With appropriate models, functions can be allocated to produce optimal designs, and assistance provided to the human inspector via job aids and training. A model was developed of the human in a two-component compound inspection task consisting of search and decision. Optimizing this model showed that the choice of optimal values of parameters in the two submodels was independent. Ten subjects were tested on a two-component inspection task, using components which had earlier been validated separately. Subjects showed some aspects of optimum behaviour, for example sub-model independence, stopping the search after an integral number of scans, and varying their decision criteria to respond to the probability and cost structure. However, in this more complex task, subjects often reverted to simpler decision rules, for example always stopping the search after one scan or accepting (or rejecting) all potential defects detected. The implication for hybrid automation systems is that humans will need help such as job aids or training if they are to perform optimally when given both search and decision tasks. [ABSTRACT FROM AUTHOR]