Behavior-based Predictive Safety Analytics - Pilot Study [supporting datasets]

Engström, Johan; Miller, Andrew; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost; Safety through Disruption (Safe-D) University Transportation Center (UTC)

doi:10.15787/VTT1/464GB9

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Behavior-based Predictive Safety Analytics - Pilot Study [supporting datasets]

2018-11-20
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By Engström, Johan ; Miller, Andrew ; Huang, Wenyan ; ...

[PDF-143.28 KB]

English

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Details:

Creators:

Engström, Johan ; Miller, Andrew ; Huang, Wenyan ; Soccolich, Susan A. ; Machiani, Sahar Ghanipoor ; ... More +

Engström, Johan ; Miller, Andrew ; Huang, Wenyan ; Soccolich, Susan A. ; Machiani, Sahar Ghanipoor ; Jahangiri, Arash ; Dreger, Felix ; de Winter, Joost Less -
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Corporate Contributors:

United States. Department of Transportation. Office of the Secretary
Subject/TRT Terms:

[+]

Automatic Data Collection Systems Behavior Crash Data Crashes Crash Risk Forecasting Data Mining Demographics Driver Behavior Driver Characteristics Drivers Driving Style Human Characteristics Logistic Regression Analysis Naturalistic Data Near Crashes Pilot Studies Risk Statistical Analysis
Publication/ Report Number:

02-020
DOI:

https://doi.org/10.15787/VTT1/464GB9
Resource Type:

Dataset
Geographical Coverage:

United States
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Abstract:

A subset of the SHRP2 data was used to construct the datasets used for the analyses in the project. The general aim of this project was to investigate and develop statistical models predicting individual driver crash involvement based on driving style, demographic and behavioral history data. For each individual driver, six consecutive calendar months were extracted beginning from the second month of data collection (study period, months 2-7). This six-month data interval was used to calculate driving style measures and crash/near crash involvement. In addition, questionnaire data for each participant, collected prior to the start of the SHRP2 data collection was retrieved. In addition, a range of further inclusion criteria were applied. In particular, drivers selected for the present analysis were required to have participated in SHRP2 data collection for at least seven months, and to have driven more than 1,000 miles in the six-month study period. This resulted in a dataset of 2,800 and 3.91 million trips, amounting to a total of 27.16 million miles driving distance and 0.69 million driving hours.
Format:

PDF
Funding:

69A3551747115/Project 02-020
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:1f0ed9b228c4757f1944d5b5a5b8638981f757fa22f7dc15e495fb7e6af07752
Download URL:

https://rosap.ntl.bts.gov/view/dot/54636/dot_54636_DS1.pdf
File Type:

Behavior-based_Predictive_Safety_Analytics_-_Pilot_Study__02-020__Dataset_Metadata.json

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