Video from user-generated content as a source of pre-crash scenario naturalistic driving data. (12th October 2020)

• Record Type: Journal Article
• Authors: St. Lawrence, Schuyler; Hallman, Jason; Sherony, Rini
• Is Part Of: Traffic injury prevention. Volume 21(2020)Suppement 1
• Page Start: S171
• Page End: S173
• Publication Date: 2020-10-12
• Subjects: Naturalistic driving -- user-generated content -- pre-crash scenarios -- video data; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2020.1829920
• Languages: English
• ISSNs: 1538-9588

Abstract

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.