Abstract
Solar glare on roads is responsible for momentaneous deterioration of a driver’s view, interfering with driving capacity and causing accidents. The situation of a driver being affected by solar glare on a road is a spatial-temporal variable, since it depends not only on the time of the day and the day of the year, which are determinant for the local Sun position (temporal factors), but also on the local slope and azimuth of the road in the driven direction (spatial factors). The present work describes a method for producing road glare maps along a road network, as well as retrieving glare information from urban roads so that solar glare vulnerability can be easily accessed. Input data are a 1m resolution Digital Surface Model from Light Detection and Ranging data and the road network. Spatial parameters are processed in a Geographic Information System environment. The Urban Glare Algorithm detects glare and outputs temporal matrices and glare maps. Shadows cast by buildings and trees are considered as well as the driver’s eyes height. The method is tested in an area of Lisbon (Portugal). This work is a contribution to road safety systems implementation and constitutes a relevant basis for warning drivers of glare through car navigation systems.
Highlights
Solar glare is an optical phenomenon that affects people whenever the angle between Sun rays and line of sight falls within their field of view (FOV)
In order to be provided with rigorous information in these situations, a high-resolution assessment of the road network is required for solar glare determination on the site and for the exact time of the accident
The presented methodology to produce high resolution solar glare maps was shown to be efficient for solar glare vulnerability studies urban areas
Summary
Solar glare is an optical phenomenon that affects people whenever the angle between Sun rays and line of sight falls within their field of view (FOV). The spatial resolution of the data (10 m) leads to inaccurate calculations of driving directions, as well as the poor vertical accuracy of the altimetric data used (± 2 m to 3 m standard deviation [15]) leads to inaccurate calculation of slopes, with direct and obvious consequences in identifying the presence of glare Apart from this aspect, during occlusion analysis only terrain elevation is considered, neglecting all urban objects that may contribute to the reduction of solar glare, like trees or tall buildings that cast shadows and reduce or eliminate the glare effect. Vulnerability to solar glare can be estimated considering both a cloudless sky (Section 3.3) and a typical overcast sky (Section 3.4)
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