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ObjectiveThis study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. BackgroundHPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. MethodA systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. ResultsThe findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. ConclusionThe study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. ApplicationThe findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.
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Identifying Important Risk Factors Associated with Vehicle Injuries using Driving Behavior Data and Predictive Analytics
Road injuries are rated among the top 10 causes of death by the World Health Organization, and the only one that is not a disease. The total economic cost of motor vehicle crashes in the United States was estimated to be $242 billion a year. This study examines multiple factors of accidents simultaneously with a goal of generating an interpretable model that can predict the occurrence of an accident given road conditions and driver behavior. The study compared 4 machine learning and deep learning modeling techniques on a dataset of 7707 trips collected by the Second Strategic Highway Research Program. A gradient boosted model was found to be most accurate and interpretable in accident prediction. This modeling technique also allows us to rank the feature importance of the factors in the model. The study finds that driver behavior, pre-incident maneuvers and secondary task duration are the most important variables in the predictive model. Using these conclusions will allow us to perform more work to infer these accident causes directly from vehicle sensor data in the future.
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- Award ID(s):
- 1741306
- PAR ID:
- 10125496
- Date Published:
- Journal Name:
- IEEE ICHI
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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