TY  - JOUR
PY  - 2020//
TI  - Video from user-generated content as a source of pre-crash scenario naturalistic driving data
JO  - Traffic injury prevention
A1  - St Lawrence, Schuyler
A1  - Hallman, Jason
A1  - Sherony, Rini
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 1538-9588
UR  - http://dx.doi.org/10.1080/15389588.2020.1829920
ID  - ref1
ER  -