More than 1.2 million people die and as many as 50 million people are injured or disabled due to road traffic injuries (RTIs) every year worldwide. The lack of reliable data hinders efforts to describe the characteristics of the issue and prioritise prevention activities. The objective was to provide a snapshot of fatal and non-fatal RTI in Argentina. We used the methodology proposed by the Global Burden of Disease Injury Expert group. External causes of deaths with unknown codes were proportionately redistributed over the known categories. In 2007 in Argentina, we estimated 5915 RTI deaths, compared with 3983 RTI deaths reported previously by the Ministry of Health, accounting for 1931 additional cases. The highest number of deaths occurred in young men (15-29 years old), although the highest RTI death rates were in the age group of 55 years and older. Four-wheeled vehicle occupants were the most common road user type killed (59.1%); vulnerable road users represented one third (29.5%) of deaths and 64% of non-fatal RTI. The national and regional estimates of RTI in Argentina should help policy makers and public-health researchers to understand the importance of RTI prevention and design specific interventions to further reduce these preventable deaths and injuries.
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http://dx.doi.org/10.1080/17457300.2011.581377 | DOI Listing |
Accid Anal Prev
January 2025
Western Australian Centre for Road Safety Research, School of Psychological Science, The University of Western Australia Perth Western Australia Australia.
Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering, The University of Mississippi, University, MS 38677, USA.
The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management.
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January 2025
Netcom Engineering S.p.A., Via Nuova Poggioreale, Centro Polifunzionale, Tower 7, 5th Floor, 80143 Naples, Italy.
This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
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