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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]

Objectives: Crashes due to motorcyclists colliding with an open car door can cause devastating injuries. In Taiwan, such crashes typically occur when the motorcyclist is travelling alongside a row of parallel-parked cars, and a driver suddenly opens the door in front of or next to the motorcycle without determining whether it is safe to do so. Injuries resulting from motorcycle door crashes tend to be severe. This study examined the factors that contribute to motorcycle door crashes. Methods: By using linked data from the National Traffic Crash Dataset and Emergency Medical Service for Taipei City for the years 2010–2015, this study estimated a mixed multinomial logit model to predict the likelihood of three types of door crashes: driver-door crashes, left passenger-door crashes, and right passenger-door crashes. Results: Data on 8237 motorcycle door crashes were extracted from the two datasets and matched. The results from the mixed multinomial logit model revealed that illegal parking, older car occupants, teenage car occupants, intoxicated car occupants and motorcyclists, and motorcycle speeding contribute to driver-door crashes; and female passengers and taxis as the type of vehicle involved in crash are associated with left passenger-door crashes. Conclusions: Our study suggested that controlling motorcycle speed, and prosecuting illegal parking and drink driving/riding may constitute effective countermeasures. The “Dutch Reach” intervention measure, which is commonly adopted in Europe for bicycle door crashes, should be applied in Taiwan to curb motorcycle door crashes, especially for elderly car occupants. [ABSTRACT FROM AUTHOR]

Pedestrians' head-turning behaviors are crucial to navigating streets safely. This research investigates the effects of phone use on pedestrians' street-crossing behaviors at an uncontrolled intersection where head-turning performances are important to identify a gap among the oncoming traffic. A field study using video cameras was conducted for evaluating pedestrians' head-turning behaviors (e.g., head-turning frequency, not looking at traffic before crossing, looking at the wrong traffic side), crossing time, and sudden movement while they were engaging in various smartphone activities (e.g., calling, texting, gaming, and listening to music). Data such as phone features, distraction types, and personal attributes of the pedestrians were obtained in the interviews conducted after pedestrians had completed crossing the street. The results indicate that the unsafe crossing behaviors (e.g., sudden movement, fewer head-turning frequencies, not looking at traffic before crossing, not looking at left side of traffic first) were more prevalent among those gaming with “Pokemon Go.” Web surfing appears to be the 2nd risky distraction event following gaming with “Pokemon Go.” Logistic regression models reveal several important correlates of unsafe crossing behaviors: being a student, large phone screen (5 in. or larger), and having un-restricted 4G Internet data allowance. The current research recommends that “Pokemon Go” gaming be prohibited while crossing the street. [ABSTRACT FROM PUBLISHER]