SWOV Catalogus

342741

Using naturalistic driving data to examine teen driver behaviors present in motor vehicle crashes, 2007-2015.
20160443 ST [electronic version only]
Carney, C. McGehee, D. Harland, K. Weiss, M. & Raby, M.
Washington, D.C., American Automobile Association AAA Foundation for Traffic Safety, 2016, 44 p., 48 ref.

Samenvatting As the driving environment continues to evolve we want to identify those crashes that teens are most frequently involved in as well as the distractions or competing activities that are most often being engaged in leading up to these crashes. However, determining what activities teens are engaging in before a crash occurs is not an easy task. Previous research has largely relied on survey and crash data to attempt to obtain this type of information. In this study, we conducted a large-scale comprehensive examination of naturalistic crash data from over 2200 moderate to severe collisions that involved teenage drivers between 2007 and 2015. The data allowed us to examine behaviours and potential contributing factors in the seconds leading up to the collision, and provided information not available in police reports. It also allowed us to look for trends associated with crashes of young drivers from 2007-2015, paying particular attention to the behaviours being engaged in leading up to those crashes. Specifically, we explored the following research questions: * Has there been a change in the prevalence of a particular crash type between 2007 and 2015? * Has there been a change in the proportion of crashes with distraction present? Has there been any change in the type of potentially distracting behaviours being engaged in? * Have eyes off forward roadway (EOFR) times changed relative to specific distractions or crash types? Crashes examined in this study involved drivers ages 16-19 who were participating in a teen driving program that involved the use of a Lytx DriveCam system. This system records video, audio, and accelerometer data when a crash or other high g-force event (e.g., hard braking, acceleration or impact) is detected. Each video is 12 seconds long, and provides data from the 8 seconds before to 4 seconds after the event. Lytx made 8228 videos of crashes that occurred between August 2007 and April 2015 available for review. In order to reduce this number and to eliminate minor curb strikes from the analysis, those crashes in which the vehicle sustained forces less than 1g were excluded. Crashes in which the DriveCam equipped vehicle was struck from behind were also excluded. Additional videos were excluded for other reasons (e.g., animal strikes, video problems, or the driver not being a teen). A total of 2229 moderate-to-severe crashes met the inclusion criteria and were analysed for the current study. Video from the 6 seconds preceding each crash were coded for analysis as it was determined to have the most potential to be contributory and allowed for comparison with previous naturalistic studies (e.g., Neale et al., 2005). A coding methodology which focused on identifying the factors present in crashes was developed specifically for gathering information from the videos. Four broad categories of coded variables included: (1) general background and environmental variables; (2) variables specific to the crash; (3) variables specific to the driver; and (4) variables specific to passengers. Each crash was double coded by two University of Iowa (UI) analysts and mediated by a third when necessary. A trend analysis was completed for the crash data from 2007 to 2015. Years 2007 and 2015 were incomplete, containing data from only a portion of the year; therefore, the trend analysis estimated the average change over a 12-month period (as opposed to a calendar year). To examine changes over time, we used linear regression models for each outcome of interest and included month and year of the crash as the continuous predictor to estimate the average change in prevalence over a 12-month period. Due to the small sample size when examining cell phone use by crash type, logistic regression was used to model each outcome of interest (cell phone use type) by year (rather than month and year) stratified by the crash type. For continuous variables (e.g., eyes off road time), linear regression was used. Overall, male drivers were present in 51.3% of the crashes and female drivers in 48.5%. The driver was seen wearing a seatbelt in 93.5% of all crashes. Passengers were present in the vehicle in one-third of crashes (34.3%), with one passenger present in 24.5% and two or more passengers present in 9.8%. Results did show a significant decline in the percentage of crashes in which passengers were present (annual average % change: -1.63 percentage points per year; 95% Confidence Interval [CI]: -2.54 — -0.72 percentage points per year). Overall, of crashes with passengers, 25.0% had at least one passenger that was unbelted. However, there was a significant trend toward increasing belt use for passengers (annual % change: 1.64; CI: 0.25 — 3.04). The majority of the passengers, when present, were estimated to be 16-19 years old (84.8%) and were male in 54.4% of crashes and female in 44.9%. In general, crashes occurred most often on collectors (52.8%). Road surface conditions were more likely to be either dry (45.5%) or covered with snow/ice (40.3%). Overall, crashes were more frequent during the week (71.5%) than on the weekend. They also occurred more frequently between the hours of 6am to 9am (18.8%) and 3pm to 6pm (26.0%), when drivers are commuting to/from school and work and more traffic is present on the roadways. The proportion of crashes occurring on arterial roads decreased significantly in 2014 and 2015 (p=0.0003) and the proportion of crashes on dry roads increased significantly (p<.0001). Trend associated with prevalence of crash types: Results showed that from 2007 to 2015 the proportion of angle crashes remained relatively consistent (annual % change: -0.28; CI: -0.99 — 0.44). However, there was a significant increase in the proportion that were rear-end crashes (annual % change: 3.23; CI: 2.40 — 4.05), thus accounting for a significant overall increase in vehicle-to-vehicle crashes. Significant reductions in both road departure (annual % change: -1.18; CI: -1.72 — -0.65) and loss of control (LOC) crashes (annual % change: -2.11; CI: -3.06 — -1.15) contributed to a corresponding significant decrease in single-vehicle crashes overall. Trend associated with prevalence of distracting behaviours: Results did not show a significant change over time in the proportion of crashes containing drivers engaged in potentially distracting behaviour. Between 2007 and 2015 an average of 58.5% of crashes contained some type of potentially distracting behaviour during the six seconds leading up to a crash. While the proportion of crashes containing a particular distraction did vary over time, the distractions that were the most common in the previous study remained the most common: attending to passengers (14.6%), cell phone use (11.9%), and attending inside the vehicle (10.7%). There were no significant increases or decreases in the proportion of crashes in which drivers were seen engaging in these behaviours. As stated, there was not a significant change in the percentage of crashes with drivers using their cell phone. However, when we looked at how drivers were using the phone, we found a significant decrease in the proportion (among all crashes) with drivers talking/listening (annual % change: -0.39; CI: -0.68 — -0.09). And, although it appears as though the proportion of crashes that involved a driver operating/looking at the phone increased over time, there was too much variability in the data to show a significant increase as a proportion of all crashes. However, among cell phone related crashes only, the proportion that involved a driver operating or looking at the cell phone, as opposed to talking/listening, increased significantly over the years examined (annual % change: 4.22; CI: 1.15 — 7.29). When cell phone use was examined by crash type over time, there was a decline in the proportion of both road departure and angle crashes in which the driver was seen talking/listening; however, neither was significant (?=-0.3968, p=0.0813; ?=-0.3533, p=0.0546). There was, however, a significant increase in the proportion of rear-end crashes with drivers operating/looking at a cell phone (?=0.1715, p=0.0262). Trend associated with eyes off forward road (EOFR) time, glance duration and reaction time: Among rear-end crashes, the average eyes off road time for the 6 seconds immediately preceding the crash significantly increased over time from 2.0s to 3.1s (?=0.1527, p=0.0004), as did the duration of the longest glance, from 1.5s to 2.1s (?=0.1020, p=0.0014). Reaction time was analysed for rear-end crashes only, and then only when the lead vehicle was moving and the brake lights were visible. Therefore, among rear-end crashes, a reaction time (including no reaction) was coded for 58.7% of crashes. Between 2008 and 2014, reaction times increased from 2.0s to 2.7s (p=0.25). Additionally, the percent of rear-end crashes in which the driver had no reaction prior to the crash increased from 12.5% in 2008 to 25.0% in 2014 (p=0.07). As the driving environment continues to evolve we want to identify those crashes that teens are most frequently involved in as well as the distractions or competing activities that are most often being engaged in leading up to these crashes. Using naturalistic driving data allows researchers a unique view into the vehicle and provides invaluable information regarding the behavioural and environmental factors present before a crash. The data gathered offers a much more detailed context relative to police reports and other crash databases, and allows more micro-level analyses to be conducted. This study examined crash data from 2007 to 2015 to determine whether there were any changes in the prevalence of particular crash types. It also explored changes in the proportion of crashes with distraction present. Additionally, trends associated with the prevalence of particular distracting activities were investigated. Finally, information was provided regarding changes in eyes-off-road time, glance durations and reaction times relative to specific distractions and crash types. Of particular interest was the increase in rear-end crashes for the teens in this study. Importantly, rear-end crashes were associated with an increase in operating/looking at the cell phone as well as an increase in the time spent engaging in this activity. While causality cannot be inferred in this study, the trend suggests that more research be conducted in the area of cell phone use, with specific regard to how and when teens are choosing to engage in this behaviour, whether it is truly causing an increase in rear-end crashes and whether existing technologies can be effective in mitigating these crashes. (Author/publisher)
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