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C H A P T E R 7 SummaryLane departure crashes make up a substantial number of motor vehicle crashes and account for a disproportionate number of fatalities. Single-vehicle ROR crashes account for almost 39% of traffic fatalities. Two-vehicle head-on crashes result in 18% of noninterchange, nonintersection fatal crashes, with 75% occurring on undivided two-lane roadways. Hence, addressing lane departure crashes is a major safety goal in the United States. Lane departure is a serious safety concern, yet the relation- ship between the factors that influence whether a vehicle departs its lane in the first place and the series of actions and events that determine the outcome are complex and not well understood. SHRP 2 is in the process of implementing a large- scale field study to collect naturalistic driving data at various locations throughout the United States. This study will result in a rich database that can be used to evaluate lane departures and better determine how the integration of driver behavior and roadway, environmental, and vehicle factors lead to dif- ferent outcomes. This research investigated which lane departure research questions can be answered when data from SHRP 2âs full-scale naturalistic driving study data become available. The focus was to determine the necessary and available data factors that could fully answer the lane departure research questions and to con- duct initial analyses of existing naturalistic driving study data to develop methods that can be used for the full-scale study. Analytical methods that could be used to answer those research questions were also explored. The focus of this research was on rural, two-lane, paved roadways. The following paragraphs summarize the information pro- vided in each chapter. Chapter 1 provided background information and outlined the scope of the research. Chapter 2 summarized the final research questions and pro- vided the results of a literature review conducted to identify driver, roadway, environmental, and vehicle factors that have some correlation to lane departure crashes. Factors identified102included horizontal and vertical curvature, roadway cross sec- tion, driveway density, illumination, weather, presence of rum- ble strips, roadway delineation and signing, pavement edge drop-off, vehicle type, speeding, influence of alcohol or drugs, driver age, and distraction. Information about the factors known to influence the like- lihood and outcome of lane departures was used to formulate a set of lane departure research questions that would be desir- able to answer if the appropriate data were available. Data sets from existing naturalistic driving studies were explored, and the most current information about the data likely to be avail- able in the full-scale study was reviewed (i.e., driver, roadway, environmental, and vehicle variables). Research questions were then categorized to distinguish those that were likely to be answered using data from the full-scale study and those that were not likely to be answered because of data limita- tions. Research questions addressed during the scope of this research were also identified. Lane departure research questions that are not likely to be feasible in the full-scale study include those that require the following factors: alcohol or drug use by the driver (alcohol sensor will be present but will not record individual use), pavement surface friction measurements, pavement edge drop- off, and quantitative measures of rain, snow, or ice on the road. Identifying the feasible research questions in Chapter 2 required information provided throughout this report, but the information was summarized in Chapter 2 for the purpose of clarity. Chapter 3 summarized the various data sets used in the research. A description of common data terms was also provided. Chapter 4 identified data elements that are expected to be necessary for answering the lane departure research questions, based on a survey of the available literature, as well as on the teamâs expertise on lane departure issues. The accuracy, fre- quency, and resolution of each data element was determined and described. The availability of the data in the UMTRI and
103VTTI databases was reviewed and the limitations described. The team also reviewed the available documentation of the work for SHRP 2 Safety Projects S03 (Roadway Measurement System Evaluation), S04A (Roadway Information Database Developer, Technical Coordination, and Quality Assurance for Mobile Data Collection), and S04B (Mobile Data Collec- tion). Based on these sources, the accuracy, frequency, and resolution of data that are expected to be available to answer the lane departure research questions in the full-scale study were evaluated. The team identified limitations and provided feedback to SHRP 2, as described in Chapter 4. Data elements were also prioritized, because resource limitations in the full- scale study will constrain data collection. Chapter 5 discussed potential lane departure surrogates that can be obtained from naturalistic driving study data. Literature regarding crash surrogates was summarized, and a method- ological approach for selecting and applying crash surrogates was outlined. Existing naturalistic driving study data were also evaluated to determine starting points for setting triggers that would identify lane departure events. The ways normal driving data may be partitioned were also evaluated using existing data. Lateral offset was compared for several driving situations. Dif- ferences were noted between driving on a tangent and on a left- or right-hand curve, between nighttime and daytime driving, and between individual drivers. As indicated in the chapter, differences are expected in what constitutes normal driving behavior. This analysis provided some guidance on stratifying normal driving by relevant variables. Chapter 6 described four analytical approaches that can be used to evaluate naturalistic driving study data and answer lane departure research questions. Several exploratory analy- sis methods were applied to data extracted from existing nat- uralistic driving studies to demonstrate ways in which lane departure research questions could be answered in the full- scale study. The intent of the analyses was to demonstrate dif- ferent analysis methods that could be used to analyze the data that will result from the full-scale study. A data sampling approach (developed by the SHRP 2 Safety Project S02 researchers) was described, and four analysis meth- ods were presented. The four approaches included (1) a data mining approach using classification and regression tree analy- sis, (2) simple odds ratio and logistic regression, (3) logistic regression for correlated data that accounts for repeatedsampling among observations (e.g., repeated sampling for the same driver, trip), and (4) a time series analysis. Three of these methods were used to evaluate existing natu- ralistic driving study data, and one method expanded on a var- ied logistic regression approach that may be better suited to the data from the full-scale study. Data were available from the UMTRI RDCW FOT that contained a number of nonconflict lane departures and samples of normal driving. Methods 1 and 2 ([1] classification and regression tree and [2] simple odds ratio and logistic regression) evaluated the likelihood of a left- or right-side lane departure. A sample-based approach was used in the classification and regression tree analysis, and an event- based approach was used for the logistic regression. Although available sample sizes were limited, both methods resulted in similar results. Both indicated that curve radius, driver age, and type of shoulder were relevant in explaining lane departures. The logistic regression also indicated that both left- and right- side lane departures were more likely to occur at night and were less likely to occur as lane width increased. The model for left- side lane departures indicated that male drivers were more likely than female drivers to be involved in a lane departure, and the model for right-side lane departures indicated that lane depar- tures are more likely on roadway sections with a higher density of lane departure crashes and for drivers who spend more time traveling 10 mph or more over the posted speed limit. The fourth method, time series analysis, used continuous data to develop a model to predict offset as a function of sev- eral vehicle kinematic variables. The method was developed and explained in such a way that it could be adapted to the full- scale study to include various explanatory variables, including driver behavior. This approach allows information, such as driver distraction in previous time periods, to be incorporated into the model. Appendices A and B describe the protocols, methods, and variable descriptions used to extract data from the UMTRI RDCW FOT and VTTI 100-car naturalistic driving study data. The method used to extract the data provided a frame- work that can be used by other researchers working with the full-scale study. Data were extracted manually, which con- sumed a large amount of resources. However, the framework can be used to automate the extraction of some data. Appen- dix A also provides a discussion about how lane departures were identified in the UMTRI data set.
