A large component of the safety research undertaken in the second Strategic Highway Research Program (SHRP 2) is aimed at reducing the injuries and fatalities that result from highway crashes. Through a naturalistic driving study (NDS) involving more than 3,000 volunteer drivers, SHRP 2 expects to learn more about how individual driver behaviour interacts with vehicle and roadway characteristics. In anticipation of the large volume of data to be collected during the NDS, several projects were conducted to demonstrate that it is possible to use existing data from previous naturalistic driving studies and data from other sources to further the understanding of the risk factors associated with road crashes. More specifically, the four S01 projects, entitled Development of Analysis Methods Using Recent Data, examined the statistical relationship between surrogate measures of collisions (conflicts, critical incidents, near collisions, and roadside encroachment) and actual collisions. This report presents the results of one of these projects, undertaken by Pennsylvania State University. It documents the second phase of a two-phase project under SHRP 2 Safety Project S01B. The primary objective of this work was to investigate structured modelling paradigms for analysis of naturalistic driving data (NDD). Five research questions were identified and various models (e.g., event-based models and categorical-outcome models) were applied to NDD to determine appropriateness for analysis and suggestions for future analyses. The following were the five research questions: 1. What is the relationship between events (e.g., crashes, near crashes, and incidents) and pre-event manoeuvres? What are the contributing driver factors, environmental factors, and other factors? 2. What hierarchical structure, if any, exists in the manner in which these relationships need to be explored? 3. What kind of elucidative evidence emerges from the analysis of roadway departure crashes in terms of Questions 1 and 2? Is the illustrative hierarchy of relationships generalisable to other non-intersection crash types, such as leading vehicle crashes? 4. In terms of elucidative evidence, what types of behavioural correlates emerge? For example, are attitudinal measurements indicative of revealed behaviour in terms of headway maintenance and speed reductions? 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 report will provide useful information for analysts of the SHRP 2 NDS data, as well as other naturalistic driving data sets. (Author/publisher)
Samenvatting