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Virginia Tech Transportation Institute (VTTI)

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https://hdl.handle.net/10919/5524

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Browsing Virginia Tech Transportation Institute (VTTI) by Department "Statistics"

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    Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health
    Stern, Hal S.; Blower, Daniel; Cohen, Michael L.; Czeisler, Charles A.; Dinges, David F.; Greenhouse, Joel B.; Guo, Feng; Hanowski, Richard J.; Hartenbaum, Natalie P.; Krueger, Gerald P.; Mallis, Melissa M.; Pain, Richard F.; Rizzo, Matthew; Sinha, Esha; Small, Dylan S.; Stuart, Elizabeth A.; Wegman, David H. (Elsevier, 2019-05)
    This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.
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    Decision-adjusted driver risk predictive models using kinematics information
    Mao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R. (Elsevier, 2021-06)
    Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.