Julie McClafferty

Chapter 6 – Naturalistic Driving Studies and Data Coding and Analysis Techniques

Publisher Summary This chapter describes the traffic conflict technique and the theory behind the power of instrumented vehicle or naturalistic driving studies, the life cycle of naturalistic driving studies, and powerful analytic techniques that can and have been used with these data. Naturalistic driving data provide powerful tools for safety researchers that incorporate some characteristics of epidemiological data analysis techniques with empirical data analysis techniques. Although these characteristics are very beneficial, they also provide novel new data and analytic methods in which to explore and study driver safety, specifically driver behavior. The life cycle of naturalistic driving studies includes the following: study design and data collection, data preparation and storage, data coding, and data analysis. Each of these steps is complex primarily due to the size and the extent of the data being collected. Naturalistic driving studies typically collect 6–8 gigabytes of video per minute, which can easily result in thousands of hours of video collected, and 6–10 TB of data that must be prepared, stored, coded, and analyzed. Naturalistic driving studies are typically lengthy and resource-intensive but worth the rich, detailed data that can be collected. These types of studies are complex and require extensive planning both prior to data collection and through the entire life cycle of the study to ensure that the initial research objectives are appropriately evaluated. Detailed planning at every step in the life cycle will result in a much easier and efficient data analysis phase of the project.

Relationship of Near-Crash/Crash Risk to Time Spent on a Cell Phone While Driving

Objectives: The objective of this study was to examine in a naturalistic driving setting the dose–response relationship between cell phone usage while driving and risk of a crash or near crash. How is the increasing use of cell phones by drivers associated with overall near-crash/crash risk (i.e., during driving times both on and off the phone)? Methods: Day-to-day driving behavior of 105 volunteer subjects was monitored over a period of 1 year. A random sample was selected comprised of 4 trips from each month that each driver was in the study, and in-vehicle video was used to classify driver behavior. The proportion of driving time spent using a cell phone was estimated for each 3-month period and correlated with overall crash and near-crash rates for each period. Thus, it was possible to test whether changes in an individual drivers cell phone use over time were associated with changes in overall near-crash/crash risk. Results: Drivers in the study spent 11.7% of their driving time interacting with a cell phone, primarily talking on the phone (6.5%) or simply holding the phone in their hand or lap (3.7%). The risk of a near-crash/crash event was approximately 17% higher when the driver was interacting with a cell phone, due primarily to actions of reaching for/answering/dialing, which nearly triples risk (relative risk = 2.84). However, the amount of driving time spent interacting with a cell phone did not affect a drivers overall near-crash/crash risk. Vehicle speeds within 6 s of the beginning of each call on average were 5–6 mph lower than speeds at other times. Conclusions: Results of this naturalistic driving study are consistent with the observation that increasing cell phone use in the general driving population has not led to increased crash rates. Although cell phone use can be distracting and crashes have occurred during this distraction, overall crash rates appear unaffected by changes in the rate of cell phone use, even for individual drivers. Drivers compensate somewhat for the distraction by conducting some of the more demanding tasks, such as reaching for or dialing a cell phone, at lower speeds. It is also possible that cell phones and other electronic devices in cars are changing how drivers manage their attention to various tasks and/or changing the kinds of secondary tasks in which they engage.

Using naturalistic driving data to identify variables associated with infrequent, occasional, and consistent seat belt use

Seat belt use is one of the most effective countermeasures to reduce traffic fatalities and injuries. The success of efforts to increase use is measured by road side observations and self-report questionnaires. These methods have shortcomings, with the former requiring a binary point estimate and the latter being subjective. The 100-car naturalistic driving study presented a unique opportunity to study seat belt use in that seat belt status was known for every trip each driver made during a 12-month period. Drivers were grouped into infrequent, occasional, or consistent seat belt users based on the frequency of belt use. Analyses were then completed to assess if these groups differed on several measures including personality, demographics, self-reported driving style variables as well as measures from the 100-car study instrumentation suite (average trip speed, trips per day). In addition, detailed analyses of the occasional belt user group were completed to identify factors that were predictive of occasional belt users wearing their belts. The analyses indicated that consistent seat belt users took fewer trips per day, and that increased average trip speed was associated with increased belt use among occasional belt users. The results of this project may help focus messaging efforts to convert occasional and inconsistent seat belt users to consistent users.

Secondary Behavior of Drivers on Cell Phones

Objectives: The objective of this study was to determine whether cell phone use by drivers leads to changes in the frequency of other types of potentially distracting behavior. There were 2 main questions of interest: (1) As each driver changes cell phone use, does he or she change the amount of driving time spent on other distracting behavior? (2) As each driver changes cell phone use, does he or she change the amount of driving time spent looking away from the driving task? Methods: Day-to-day driving behavior of 105 volunteer subjects was monitored over a period of 1 year. The amount of driving time during each trip spent on tasks secondary to driving (or looking away from the driving task) was correlated to the amount of time on a cell phone, taking into account the relationships among trips taken by the same driver. Results: Drivers spent 42% of the time engaging in at least one secondary activity. Drivers were talking on a cell phone 7% of the time, interacting in some other way with a cell phone 5% of the time, and engaging in some other secondary activity (sometimes in conjunction with cell phone use) 33% of the time. Other than cell phone use, the most common secondary activities were interacting with a passenger (12% of driving time), holding but not otherwise interacting with an object (6%), and talking/singing/dancing to oneself (5%). Drivers were looking straight forward 81% of the time, forward left or right 5% of time, in a mirror 4% of the time, and elsewhere (eyes off driving task) 10% of time. On average, for each 1 percentage point increase in cell phone talking, the other secondary behavior rate decreased by 0.28 percentage points (P <.0001), and the rate of eyes off driving task decreased by 0.02 percentage points (P =.0067). For each 1 percentage point increase in the amount of other cell phone interaction per trip, the other secondary behavior rate decreased by 0.08 percentage points (P =.0558), but the rate of eyes off driving task increased by 0.06 percentage points (P <.0001). Conclusions: Although using a cell phone can be distracting from the driving task, other secondary activities can be equally or more distracting, at least as measured by eye glances away from the road ahead and mirrors. In this group of drivers, dialing, reaching for, and answering the cell phone were associated with increased eyes off driving task, as opposed to the decrease in eyes off driving task associated with talking on the phone. Predictions about the effect of cell phone usage on driver distraction need to consider what other behavior is being displaced by the time spent on the phone. A focus by researchers, policy-makers, and the media on the distraction of using cell phones while driving may lead drivers to disregard the risk of other secondary behavior that is even more distracting.