@article{ref1,
title="Video from user-generated content as a source of pre-crash scenario naturalistic driving data",
journal="Traffic injury prevention",
year="2020",
author="St Lawrence, Schuyler and Hallman, Jason and Sherony, Rini",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: The objective of this study was to investigate the use of public video from internet user-generated content as a means of collecting naturalistic driving data.   METHODS: A convenience sample of 38 videos comprised of 203 events was extracted from publicly available channels on the YouTube™ platform. Each event was manually reviewed and pseudo-coded according to a subset of current CRSS variables. Pre-crash scenarios were coded using categories developed for prior NHTSA analysis.   RESULTS: Crashes represented 67% of the reviewed cases. Collisions with motor vehicles accounted for 84% of all crashes in the sample. Pre-crash scenarios were able to be determined for all crashes and near-crashes. The most prevalent pre-crash scenario types in the video data were Crossing Paths (41%), Rear End (21%), and Lane Change (17%). The top pre-crash scenarios from Swanson et al., were Rear End (31%), Crossing Paths (21%), and Lane Change (12%). The most prevalent pre-near crash scenario types in the video data were Crossing Paths (32%), Lane Change (30%), and Pedestrian (12%).   CONCLUSIONS: The most prevalent pre-crash scenarios in the video data were similar to those in data from FARS and NASS-GES. Though not nationally representative, this preliminary study demonstrated that user-generated content may be useful as a source of inexpensive naturalistic data and provides sufficient detail to capture important pre-crash, near-crash and crash information.<p /> <p>Language: en</p>",
language="en",
issn="1538-9588",
doi="10.1080/15389588.2020.1829920",
url="http://dx.doi.org/10.1080/15389588.2020.1829920"
}