Behavior-based Predictive Safety Analytics: Pilot Study

Johan Engström, Andrew Miller, Wenyan Huang, Susan Soccolich, Sahar Ghanipoor Machiani, Arash Jahangiri, Felix Dreger, Joost de Winter

Research output: Book/ReportReportScientific

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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.
Original languageEnglish
PublisherSan Diego State University
Commissioning bodyU.S. Department of Transportation
Number of pages94
Publication statusPublished - 2019

Keywords

  • Crashes
  • driver behavior
  • driver characteristics
  • data mining
  • driving style
  • risk
  • naturalistic data

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