Analysis of Existing Data: Prospective Views on Methodological Paradigms (2012) / Chapter Skim
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1Background In the spring of 2007, Pennsylvania State University (Penn State) was awarded a contract to analyze existing naturalistic databases as part of the S01 Safety project within the second Strategic Highway Research Program (SHRP 2)
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2distracted just before the event and whether the vehicle crossed over the lane or road edge. One may think of these models as exploring the details of factors associated with the events.
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3One of the most important outcomes of the UMTRI modeling effort is the successful estimation of cohort models using homogeneous trip segments. This formulation takes advantage of the unique trip-by-trip data obtained in the naturalistic study, along with geographic information system (GIS)
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4The efficacy of using categorical-outcome models (such as logit or binary hierarchical models) to compare crash and noncrash events was explored within the limits of the VTTI data by comparing crash and near-crash events (combined)
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5This driver-based hierarchical model presents another example of how hierarchical approaches can be applied to naturalistic data. The benefits of obtaining gender-specific estimates of factors contributing to the risk of events are clear.
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6variables were strong indicators of crash or near-crash events; in hierarchical models subject over lane or road edge was the second strongest predictor associated with a crash or near-crash event. While this measure has a strong association with crash events, this measure does not have a time dimension, so it does not directly meet Shankar's desirable criteria (Shankar 2008)
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7annual mileage as exposure. These models showed that exposure is essential to the study of the expected number of events per year for drivers.
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8Research Question 5 If elucidative evidence does in fact emerge in terms of attitudinal correlates and how their interactions vary by context, is it plausible to parse out the marginal effects of various context variables on crash risk by suitable research design? This question bears directly on the importance of context in the analysis of naturalistic driving data.
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