Citation

Li X, Cai BY, Qiu W, Zhao J, Ratti C. Transp. Res. C Emerg. Technol. 2019; 106: 132-144.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.trc.2019.07.013

PMID

unavailable

Abstract

The sun glare is one of the major environmental hazards that cause traffic accidents. Every year many traffic accidents are caused by sun glare in the United States. Providing accurate information about when and where sun glare happens would be helpful to prevent sun glare caused traffic accidents. In this study, we proposed to use the publicly accessible Google Street View (GSV) panorama images to estimate and predict the occurrence of sun glare. GSV images have view sight similar to drivers, which make GSV images suitable for estimating the visibility of sun glare to drivers. A recently developed convolutional neural network algorithm was used to segment GSV images and predict obstructions on sun glare. Based on the predicted obstructions for given locations, we further estimated the time windows of sun glare by calculating the sun positions and the relative angles between drivers and the sun for those locations. We conducted a case study in Cambridge, Massachusetts, USA.

RESULTS show that the method can predict the occurrence of sun glare precisely. The proposed method provides an important tool for people to deal with the sun glare and reduce the potential traffic accidents caused by the sun glare.

Language: en

Keywords

Deep learning; Google Street View; Sun glare