Classification of Street Lighting Conditions for a Community-Centric System

Abstract

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.