A novel method for predicting and mapping the occurrence of sun glare using Google Street View
Name
1808.04436.pdf
Description
Submitted version
Size
4.92 MB
Format
Adobe PDF
Checksum (MD5)
3730f7512702aac00e59a113729da107
Author(s)
Li, Xiaojiang
Cai, Bill Yang
Qiu, Waishan
Zhao, Jinhua
Ratti, Carlo
Date Issued
2019
Journal
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Version
Original manuscript
Abstract
© 2019 Elsevier Ltd 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.
MIT Department
Senseable City Laboratory
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.TRC.2019.07.013
