Behavior-based Predictive Safety Analytics – Pilot Study

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)

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Behavior-based Predictive Safety Analytics – Pilot Study

2019-04-01
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By Engström, Johan ; Miller, Andrew ; Huang, Wenyan ; ...

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English

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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
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Research Report
Corporate Publisher:

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

This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
Format:

PDF
Funding:

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

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:58fddd76eee8f528ef4a9538026e8a2b55813071aec4ad4d4fa9d14281e3ce8e
Download URL:

https://rosap.ntl.bts.gov/view/dot/49167/dot_49167_DS1.pdf
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Behavior-based Predictive Safety Analytics – Pilot Study

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