Introduction: Road traffic injuries (RTIs) are the most serious health issue in the world and the main cause of death after the first year of birth. Environmental factors are among the most important and effective aspects of RTIs occurrences and their human consequences.
Aim: This study aimed to identify the environmental factors associated with road traffic accidents (RTAs) in Ilam province.
Methods: All crash data in Ilam province in 2012 were investigated. RTI's data was collected using COM 114 form which included some human, environmental and vehicle related factors. Through Chi-Square test and logistic regression model, the association between environmental factors and RTIs was examined. For all statistical analyses we used the package Stata 13.1.
Results: A total of 2314 traffic accidents were investigated. The highest 1659 (72.3%) and lowest 19 (0.83%) frequency of RTAs occurred during day and sunrise respectively. The majority of RTAs took place in July and September. The maximum number of RTAs was when the day was clear (91.7%) 2103 and the minimum when it was cloudy (3.5%) 81. A significant relationship was found between fatal RTAs and factors such as; the sort of the road, the hindered visibility, the location of the accident, the accidents' place, the climate, and lighting of the day (P<0.05). The adjusted chance of traffic accidents causing death or injuries in main streets was 9.7 times more than in highways; in sidetracks it was 3.54 times more. And when it was cloudy, the chance was 2.60 times more than when was clear (P<0.05).
Conclusion: Regarding the standards of road construction, development of the roads, and educating drivers how to adjust their driving behavior to the environment and road conditions could have a great role in decreasing RTAs causing death or injury.
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http://dx.doi.org/10.5455/medarh.2019.73.169-172 | DOI Listing |
Int J Emerg Med
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January 2025
Hochschule für Technik und Wirtschaft Berlin (HTW Berlin), Berlin, Germany.
Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources.
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