ABSTRACT
BACKGROUND: Motorcyclists are one of the most vulnerable groups in road accidents. This study aimed to investigate the effective measures to reduce injuries and deaths in the most vulnerable road users' motorcyclists in 2020. METHODS: The study was a cross-sectional study. In this study, 147 motorcycles were randomly selected from the list of all motor courier units in Tehran, which was prepared through an internet search. The required information was collected through questionnaires and interviews. The inclusion criteria had a minimum high school education and an age range of 18-65 years. The trained questioners referred to the selected courier offices and after obtaining consent to participate in the study, project questionnaire were completed. RESULTS: The mean age of subjects was 31.4 ± 8.0 years. All subjects were male. The purpose of the trip was going to work (84.4%). The highest percentage of leaving home time (94.6%) was observed in the morning. More than half of the subjects had a history of accidents (54.5%), and also more than half of the subjects (54.0%) had a history of an accident in their 2nd degree relatives' families. Forth-fifths of the studied motorcyclist (89.5%) wore helmets. Nearly half of the subjects (48.3%) always fastened their helmets strap. The highest action (72.0%) was suggested to separate the motorcycle lanes. CONCLUSION: In the viewpoint of the motorcyclist, separating the motorcycle lines could be one of the most effective measurements to injury reduction. Hence, officials and planners need to pay more attention to the logical demands of motorcyclists.
ABSTRACT
Since fallen trees are a key factor in biodiversity and biogeochemical cycling, information about their spatial distribution is of use in determining species distribution and nutrient and carbon cycling in forest ecosystems. Ground-based surveys are both time consuming and labour intensive. Remote-sensing technology can reduce these costs. Here, we used high-spatial-resolution aerial photographs (0.5-1.0 cm per pixel) taken from an unmanned aerial vehicle (UAV) to survey fallen trees in a deciduous broadleaved forest in eastern Japan. In nine sub-plots we found a total of 44 fallen trees by ground survey. From the aerial photographs, we identified 80% to 90% of fallen trees that were >30 cm in diameter or >10 m in length, but missed many that were narrower or shorter. This failure may be due to the similarity of fallen trees to trunks and branches of standing trees or masking by standing trees. Views of the same point from different angles may improve the detection rate because they would provide more opportunity to detect fallen trees hidden by standing trees. Our results suggest that UAV surveys will make it possible to monitor the spatial and temporal variations in forest structure and function at lower cost.
