Traffic injury prevention
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Traffic injury prevention · Jan 2020
The electric scooter: A surging new mode of transportation that comes with risk to riders.
Objective: The proliferation of electric scooter sharing companies has inundated many municipalities with electric scooters. The primary objective of this study is to characterize the epidemiology of injuries from this new mode of transportation in order to inform injury prevention efforts. Methods: A multicenter, retrospective study was conducted at two level 1 trauma centers in an urban setting. ⋯ There was a lack of safety equipment utilization and concomitant alcohol utilization was common. These may offer areas of focus for injury prevention efforts. Additionally, standardization of injury coding for electric scooter related injury is critical to future studies and will help better understand the impact of this new mode of transportation.
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Traffic injury prevention · Jan 2020
Analysis of drivers' deceleration behavior based on naturalistic driving data.
Objective: As one of the bases for designing a humanlike brake control system for the intelligent vehicle, drivers' deceleration behavior needs to be understood. There are two modes for drivers' deceleration behavior: (i) brake pedal input, by applying brake system to reduce the speed; (ii) no pedal input, by releasing the accelerator pedal without pressing the brake pedal, thus decelerating by naturalistic driving resistance. The deceleration behavior that drivers choose to press the brake pedal has been investigated in previous studies. ⋯ Conclusion: The drivers' deceleration behavior can be divided into "no pedal input" and "brake pedal input." The following six factors significantly affect drivers' choice of deceleration mode: Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. The logistic regression model can quantify the influence of these six factors on drivers' deceleration behavior. This study provides a theoretical basis for the braking system design of ADAS (Advanced Driving Assistant System) and intelligent control system.
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Traffic injury prevention · Jan 2020
Mapping fractures from traffic accidents in Sweden: How do cyclists compare to other road users?
Introduction: Cyclists account for a large share of injured road users in traffic. The crash data analysis for cyclist safety and protection should be based on a representative dataset of real-world crashes. This manuscript aimed to explore the patterns of cyclists' fractures and factors associated with fractures of higher severity. ⋯ Fractures of cyclists to the acetabulum (100%), pelvis (84.2%), vertebra (75%) and tibia (70.3%) were most frequently high energy fractures. Single bicycle incidents (OR = 0.165) and collisions with another bicycle (OR = 0.148) were significantly less likely to result in a high energy fracture than a collision with a car. Conclusions: The results of this study may guide the design of appropriate protective devices for the cyclists based on the different injury mechanisms and provide implications for prioritizing new countermeasures, campaigns, or regulations.
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Traffic injury prevention · Jan 2020
Development of an expert derived ICD-AIS map for serious AIS3+ injury identification.
Objective: The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine 'serious' (MAIS3+) road traffic injuries. ⋯ Conclusions: The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.
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Traffic injury prevention · Jan 2019
What can a hazard function teach us about drivers' perception of hazards?
Hazard perception (HP) is typically defined as the ability to read the road and anticipate hazardous situations. Several studies have shown that HP is a driving skill that correlates with traffic crashes. Measuring HP differences between various groups of drivers typically involves a paradigm in which participants observe short videos of real-world traffic scenes taken from a driver's or a pedestrian's perspective and press a response button each time they identify a hazard. Young, inexperienced drivers are considered to have poor HP skills compared to experienced drivers, as evident by their slower response times (RTs) to road hazards. Nevertheless, though several studies report RT differences between young, inexperienced and experienced drivers, other studies did not find such differences. We have already suggested that these contradictory findings may be attributed to how cases of no response-that is, a situation where a participant did not respond to a hazard-are being treated. Specifically, we showed that though survival analysis handles cases of no response appropriately, common practices fail to do so. These methods often replace a case of no response with the mean RT of those who responded or any other central tendency parameters. The present work aims to show that treating cases of no response appropriately as well as selecting a distribution that fits the RT data is more than just a technical phase in the analysis. ⋯ The suggested process has the ability to provide researchers with additional information regarding the nature of the traffic scenes that enables differentiating between various hazardous situations and between various users with different characteristics such as age or experience.