This paper proposes a method for collecting road lighting situation at night for recommending safety walking route. In recent years, there is a growing demand for public route information service. Among them, the lighting situation, i.e. brightness of a road at night is important information for public safety. Such information is collected and visualized by some municipalities, but the coverage of the survey is not sufficient due to measurement equipment costs and labor costs. Other researches focus on lighting situation of street lamps, but these researches ignores the influence of other lights to the road illumination such as vending machines. In order to estimate lighting situation of a road, this paper proposes three attributes, which are calculated from illuminance data collected from a road. Illuminance data is collected using a light sensor of a smartphone. In order to evaluate the effectiveness of these attributes, k-NN and Naive Bayes classifiers are built from actual road illumination data.1
This paper proposes a method for classifying street lighting conditions after dark in order to share the collected data with the local community. Such information is important for the safety and security of residents, and can be used to discuss about anti-crime activities and nighttime route recommendations. However, it is difficult to ascertain the actual street lighting conditions because of insufficient street-lamp data and the effects of obstacles and other light sources. In order to tackle this problem, we propose a social approach by which local residents collaboratively collect street lighting conditions using their smartphones. The technology behind this approach is a classifier that places the street lighting conditions into one of three levels. It is based on three attributes that are calculated from the illuminance data collected by the smartphones. The results of experiments on 164 actual streets show a maximum classification accuracy of 88.4%. We also discuss performance differences between smartphones and the effect of walking speed during data collection, both of which are important factors affecting the classification accuracy.
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