Road traffic injuries (RTIs) continue to increase with the proliferation of motor vehicles, especially in low-income countries where safe road infrastructure is lacking. Knowing where and why RTIs occur would allow for increased safety and prevention planning. In this study, police records of 300 motor vehicle collisions which occurred between February 2013 and January 2014 in Moshi, Tanzania were reviewed. Analysis of variables including victim age, gender, type of collision, conditions, and use of safety equipment were analyzed. Geographic information system (GIS) analysis was performed to identify areas with the most collisions. Most injuries occurred at four intersections on two main corridor. Car crashes represented 48% of reports while motorcycle collisions were 35% of reports. Victims were predominantly male. The majority (64%) of RTI victims in cars used seatbelts while only 43% of motorcyclists wore helmets; none of those who used the helmet or seatbelt suffered a grievous injury. These data demonstrate that RTIs in Moshi occur in predictable high traffic locations. RTIs injure victims of all backgrounds and safety equipment is not universally utilized. More investment is needed in improved data collection methods, and a greater emphasis on intersection safety is needed to reduce these preventable injuries.
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http://dx.doi.org/10.1016/j.injury.2017.05.004 | DOI Listing |
Sensors (Basel)
December 2024
School of Information and Communication, Guilin University of Electronic Technology, 1 Xiamen Road, Guilin 541004, China.
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December 2024
CARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
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December 2024
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features.
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December 2024
School of Highway, Chang'an University, Middle Section of South Erhuan Road, Xi'an 710064, China.
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