Browsing by Author "Jovanis, Paul P."

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Cohort-Based Analysis Structure for Modeling Driver Behavior with an In-Vehicle Data Recorder
(2016)
Wu, Kun-Feng
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Jovanis, Paul P.
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管理學院
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College of Management
In recent years, the increasing availability of in-vehicle data recorder technologies has resulted in more safety studies that have unobtrusively observed the behavior of drivers during actual driving. The research in this paper proposes and applies a flexible analysis structure that can be applied to data that reflect events of interest including crash data and safety critical event data from naturalistic driving studies and alert warnings from onboard safety warning systems. The fundamental requirements for the data include a need for GPS and a geographic information system to allow positioning the vehicle on a network and the occurrence and recording of events of interest with attributes, which may include those reflecting the roadway, environment, driver, and event. A distinguishing feature of the formulation is the explicit inclusion of exposure to risk. The analysis approach responds to four challenges facing researchers using in-vehicle data recorder data to assess driving behavior with surrogate safety performance measures: inclusion of exposure to risk; allowance for inclusion of driver, event, environment, and roadway attributes in a structured formulation; modeling of driving exposure in different contexts, including both roadway and environment; and allowance for comparison to a baseline hazard where no events occur. A hierarchical mixed-effects Poisson regression model is used with data from University of Michigan Transportation Institute\'s curve speed warning system field operation test in the application of the method. The model identifies drivers who have significantly higher alert frequency than similar drivers, both for all driving and for driving on major and minor arterials. The paper concludes with a discussion of potential applications beyond field operational test settings, which include analysis of crash and other naturalistic driving study observed events.
Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level
(2014-11-01)
Wu, Kun-Feng
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Aguero-Valverde, Jonathan
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Jovanis, Paul P.
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
There has been considerable research conducted over the last 40 years using traffic safety-related events to support road safety analyses. Dating back to traffic conflict studies from the 1960s these observational studies of driver behavior have been criticized due to: poor quality data; lack of available and useful exposure measures linked to the observations; the incomparability of self-reported safety-related events; and, the difficulty in assessing culpability for safety-related events. This study seeks to explore the relationships between driver characteristics and traffic safety-related events, and between traffic safety-related events and crash involvement while mitigating some of those limitations. The Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study dataset, in which the participants\' vehicles were instrumented with various cameras and sensors during the study period, was used for this study. The study data set includes 90 drivers observed for 12-13 months driving. This study focuses on single vehicle run-off-road safety-related events only, including 14 crashes and 182 safety-related events (30 near crashes, and 152 crash-relevant incidents). Among the findings are: (1) drivers under age 25 are significantly more likely to be involved in safety-related events and crashes; and (2) significantly positive correlations exist between crashes, near crashes, and crash-relevant incidents. Although there is still much to learn about the factors affecting the positive correlation between safety-related events and crashes, a Bayesian multivariate Poisson log-normal model is shown to be useful to quantify the associations between safety-related events and crash risk while controlling for driver characteristics. (C) 2014 Elsevier Ltd. All rights reserved.