Driver inattention is a major contributor to highway crashes. The National Highway Traffic Safety Administration estimates that at least 25% of police-reported crashes involve some form of driver inattention. Driver distraction is one form of inattention and is a factor in over half of these crashes. Distraction occurs when a driver “is delayed in the recognition of information needed to safely accomplish the driving task because some event, activity, object, or person within or outside the vehicle compels or induces the driver’s shifting attention away from the driving task.” The presence of a triggering event distinguishes a distracted driver from one who is simply inattentive or “lost in thought.” The AAA Foundation for Traffic Safety awarded a contract to the University of North Carolina Highway Safety Research Center to conduct research on the role of driver distraction in traffic crashes. The goal of the project is to identify the major sources of distraction to drivers and the relative importance of the distractions as potential causes of crashes. This report presents the results of Phase I of the project (For Phase II see C 25997). Included is a descriptive analysis of five years of the National Accident Sampling System (NASS) Crashworthiness Data System (CDS) data, along with an analysis of narratives for two years for both CDS and North Carolina data. The descriptive analyses and the narrative analysis were done to provide input for developing a more comprehensive taxonomy of driver distractions; the taxonomy will guide future field data collection efforts. The CDS is an annual probability sample of approximately 5,000 police-reported crashes involving at least one passenger vehicle that has been towed from the crash scene. Data are collected by trained, professional crash investigation teams that collect information at the scene of the crash, from an examination of the crash-involved vehicles, directly from interviews with the crash victims and other witnesses, as well as from available medical records. Beginning in 1995, a variable for coding the “Driver’s Distraction/Inattention to Driving” was added to the CDS. The variable contains codes for attentive, looked but did not see, and sleepy, along with more than a dozen specific distractions (eating or drinking, other occupants, moving object in vehicle, talking on cellular phone, etc.). For the current analyses two variables were defined – one identifying the attention status of the driver (attentive, distracted, looked but did not see, sleepy/asleep, or unknown), and the second the specific distracting event for those drivers identified as distracted. The CDS driver distraction data is vehicle rather than crash oriented and consequently it underestimates the role of distraction in actual crashes. For the overall 1995-1999 CDS data, 48.6% of the drivers were identified as attentive at the time of their crash; 8.3% were identified as distracted, 5.4% as “looked but did not see,” and 1.8% as sleepy or asleep. The remaining 35.9% were coded either as unknown or no driver present. This high percentage of drivers with unknown attention status has the effect of diluting the percentages in the other categories. Without the unknowns, the percentage of drivers identified as distracted increases to 12.9%. The percentage of actual crashes involving driver distraction would be still higher. The specific sources of distraction among distracted drivers were: Specific Distraction % of Drivers Outside person, object or event 29.4 Adjusting radio, cassette, CD 11.4 Other occupant in vehicle 10.9 Moving object in vehicle 4.3 Other device/object brought into vehicle 2.9 Adjusting vehicle/climate controls 2.8 Eating or drinking 1.7 Using/dialling cell phone 1.5 Smoking related 0.9 Other distraction 25.6 Unknown distraction 8.6 ______ 100.0 Percentages for the different types of distractions should be viewed as preliminary estimates that are likely biased by differential underreporting. These are research results that will be useful in building a broader understanding of driver distraction. The percentages for the different types of distractions should not be used to guide policy development. Young drivers (under 20 years of age) were the most likely to be involved in distraction-related crashes. In addition, certain types of distractions were more prominent in certain age groups, for example, adjusting the radio, cassette or CD among the under 20-year-olds; other occupants (e.g., young children) among 20-29 year-olds; and outside objects and events among those age 65 and older. Variations by driver sex were less pronounced, although males were slightly more likely than females to be categorised as distracted at the time of their crash. In addition to these driver factors, a number of roadway, environmental, vehicle, and crash characteristic variables were also examined to determine their relationship to driver distraction. Although these results were less conclusive, they nevertheless underscore the importance of taking into account specific contextual factors in collecting and analysing driver distraction data. A few illustrative examples include the higher proportion of adjusting radio/cassette/CD events occurring in night-time crashes, the higher proportion of moving object in vehicle events occurring in crashes on non-level grade roadways, and the higher proportion of other occupant distractions occurring at intersection crashes. To obtain further insight into the specific events falling into each of the identified CDS categories, two years of narrative CDS data were reviewed. In addition, a computerised search was made of two years of North Carolina police-reported crash narratives. Both activities proved helpful in developing a more complete taxonomy of events distracting drivers. When interpreting the results of this Phase I analysis, it is important to keep in mind both the purpose for which it was conducted, and the limitations inherent in the data. The primary purpose of the analysis was to provide input for the development of a more comprehensive taxonomy of driver distractions and to understand important contextual variables. The data limitations are considerable and include potential underreporting of distracted driving in general as well as differential underreporting of specific distracting events. These results suggest that demographic and situational factors are related to driver distraction. Additional research is needed to quantify the frequency and intensity of different driver distractions and to understand how other variables affect distractability and willingness to engage in distracting behaviours. As roads grow more congested and the demands on drivers increase, it seems likely that new in-vehicle technologies will add even more potential distracters. (Author/publisher) See also C 22780 (ITRD E206577).
Abstract