Along with population aging phenomenon, the problems of traffic safety of elderly people is increasingly worsening. Unsurprisingly, numerous studies on this issue have been carried out. However, a meta-review to find out global trends of this has not been observed. The goal of this study is to depict an overall picture of the literature based on a meta revision of well over one hundred related studies, in which the most common findings as well as shortcoming issues are identified. A further effort is made to validate those findings in the case of Japan. The results show that aged people are at a remarkably high risk of traffic accidents largely due to their higher frequency of failures, especially among non-automobile travelers. Their failures could be often resulted from the neurological and physical impairments because of the aging effects. Analyses made on statistical data of Japan showed that Japanese older drivers were not in the same situation as their counterparts in other nations, that is they did not have a higher frequency of failures compared to their young counterparts. Interestingly, improper steering and/or braking was the most common cause of failures which result in traffic accident among the elderly, compared to careless driving of all other age groups. From these findings, suggestions for future research to improve traffic safety situation of the elderly in general, and in particular Japanese context are proposed.
Under the effects of climate change, especially the increase in rainfall intensity, the landslide has recently made a lot of consequences. Therefore, slope failure forecasting has been viewed as a key task to save human lives and economic losses. In the past, slope monitoring was often carried out manually directly on-site, therefore very difficult to get data continuously for a long time, with a too low temporal frequency that does not permit precise forecast. Recently, applying IoT (Internet of Things) technology in monitoring has begun popular. This technology allows data can be automatically gathered, updated and transmitted via the internet in real-time. This not only permits a more precise forecast but also prompt responses before the failure really happen. This paper introduces an IoT integrated model for forecasting the time of failure based on displacements. The detailed guidelines of theoretical basis, how to install devices and how to collect, process and transmit data are represented, so this would be easy and convenient to apply in practice. A small-scale physical model was made to test the function of the system. The simulation tests indicated good performance.
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