Naturalistic driving, also known as naturalistic observations, is a traffic research method, pioneered by the Virginia Tech Transportation Institute (VTTI), in the United States (Regan et al., 2012). Specifically, the 100-Car NDS, conducted by the VTTI and sponsored by the National Highway Traffic Safety Admin (NHTSA), was a ground- breaking work since it was the first instrumented vehicle study aiming to gather a large volume of naturalistic driving data from many drivers over a long period of time (Dingus et al., 2006). An NDS can be defined as “A study undertaken to provide insight into driver behaviour during everyday trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control” (Eenink et al., 2014). Existing methods for collecting data on driver performance and behaviours such as questionnaires and controlled experiments are inferior to NDS because, in naturalistic driving studies, the data are a mixture of normal and safety-critical situations and are gathered in uncontrolled, thus natural, conditions (Regan et al., 2012). More research attention has focused on
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studying not only safety-critical but also normal conditions (Baldanzini et al., 2010), making NDS a really valuable research method for data gathering.
Typically, in a naturalistic observation study, passenger cars are equipped with devices, various data-logging instruments (e.g. radars, lidars, sensors, GPS, cameras, and accelerometers) that continuously monitor various aspects of driving behaviour including information about:
• vehicle movements (acceleration, deceleration, speed) • the driver (eye, head and hand movements)
• and the direct environment (traffic densities, THW, road and weather conditions)
What gives NDS an advantage against other methods is that its purpose is to observe (individual) road user behaviour in the driver’s everyday driving life (Research., 2012). Specifically, the drivers are not affected at all from the study since they are not given any special instructions, no experimenter is present, and the data collection instrumentation is unobtrusive (Neale et al., 2005). Studies in the United States show that Naturalistic Driving provides very interesting information about the relationship between drivers, road, vehicle, and weather and traffic conditions. It is important to display other conventional methods of data collection and their advantage and disadvantage (Figure 3.1) in order to gain a deeper understanding of the benefits of the naturalistic driving data (Baldanzini et al., 2010; Regan et al., 2012; Research., 2012).
Specifically, controlled experiments, i.e. simulator studies and test tracks have the advantage to obtain large control on the examined variables and the traffic environment (SWOV Institute for Road Safety Research, Leidschendam, 2012) (Figure 3.1). For example, with these methods, researchers can focus on the variable of interest by changing it, conducting multiple experiments and comparing the results. On the other hand, there is concern that with these methods the drivers do not always drive as they do in real-world and simulators and test tracks cannot mimic exactly the combination of complex driving environments and driver behaviours (Regan et al., 2012). By using questionnaires, information that is difficult accessible can be obtained,
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especially for the driver’s personality and attitude, although the degree of truth is questionable (Baldanzini et al., 2010; SWOV Institute for Road Safety Research, Leidschendam, 2012) (Figure 3.1). Regarding the epidemiological research into crashes, it can provide a large amount of information about crashes which although doesn’t give sufficient insight and detail to reveal the factors affecting the crash (SWOV Institute for Road Safety Research, Leidschendam, 2012).
Figure 3.1: Conventional methods for data collection in studying driving
In summary, these data collection methods are limited with respect to the depth and quality of information they provide - especially information about human factors (Regan et al., 2012). The NDS method overcomes a range of those problems associated with traditional approaches to data collection as it provides information about normal behaviour and about all types of crashes and near-crashes, which were unreported with the other data collection methods (Regan et al., 2012; SWOV Institute for Road Safety Research, Leidschendam, 2012). Moreover, it allows for direct observation of driver behaviours and of the factors that result in different events, e.g. deceleration, acceleration, turning etc.
However, there are also some challenges associated with the NDS method. First, it is very resource-demanding in terms of sample recruitment, data gathering, data storage and data analysis. Also, the same problem as in simulators may appear but in a smaller degree, i.e. the behaviour of the driver may be influenced by having in mind that there are cameras and other sensors monitoring every action. Furthermore, due
•Large degree of control over the variables (+) •Difficult transfer of the results to actual traffic (-)
Controlled experiments
•Access to difficult accessible information (+)
•Doubt that the self-reported behaviour corresponds to actual behaviour (-)
Questionnaires
•Valuable information about crashes (+)
•It is solely derived from indirect sources, like the police data about crashes (-)
Epidemiological research into crashes
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to the fact that crashes are rare events, a very large sample size of traffic data is needed to obtain a sufficient number of crash-events (Regan et al., 2012).
Some Naturalistic driving studies and their objectives are:
✓ 100-Car NDS in the United States. A primary goal of this study was to provide vital exposure and pre-crash data in order to understand the causes of crashes, refine the crash avoidance countermeasures and use them to reduce crashes and their consequences. The most important outcome of this study was that in almost 80 per cent of all the crashes observed in this study, distraction or inattention played a role (Neale et al., 2005).
✓ Strategic Highway Research Program 2 (SHRP2). SHRP 2 was created to find solutions to four strategic focus areas: the role of human behaviour in highway safety; rapid renewal of ageing highway infrastructure; congestion reduction through improved travel time reliability; and transportation planning that better integrates community, economic, and environmental considerations into new highway capacity.
✓ INTERACTION project. Its main objective was to understand driver interactions with in-vehicle technologies. It studies why, how and when drivers use intelligent technologies in their vehicle and their effect on driving behaviour. The technologies that are studied are: cruise control, mobile phone, navigation systems and speed limiters.
✓ PROLOGUE (PROmoting real Life Observations for Gaining Understanding of road user behaviour in Europe). PROLOGUE aims to assess the feasibility and usefulness of a large-scale European NDS and to create a market for this type of research. Benefits and feasibility are partly determined by five field studies focusing on various aspects of road safety, such as the everyday driving behaviour of novice drivers, cyclists and pedestrians (Research., 2012).
✓ DaCoTA. It is intended to provide policymakers and other stakeholders in Europe regarding road safety and methods for data collection and processing. ✓ 2-BE-SAFE project. The aim of the 2-BE-SAFE project was to design and implement a broad-ranging research program that produces fundamental knowledge on Powered Two-Wheeler (PTW) riding behaviour, performance, and safety, when being alone and when interacting with other road users. Also,
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it aims in the development of a broad and integrated package of public policies for improving the safety of PTW riders in Europe.
✓ UDRIVE (European naturalistic Driving and Riding for Infrastructure and Vehicle safety and Environment). UDRIVE is the first large-scale European NDS on cars, trucks and PTWs. The UDRIVE project builds further on the experience of the PROLOGUE feasibility study and various FOTs and follows the Field opErational teSt supporT Action (FESTA)-V methodology. This 57 months project is funded under the 7th EU Framework Programme and the
project partners are the SWOV (coordinator), BASt, CDV, CEESAR, CIDAUT, DLR, ERTICO, FIA, IBDIM, IFSTTAR, KFV, LAB, Or Yarok, Loughborough University, SAFER, TNO, TU Chemnitz, University of Leeds and VOLVO. It aims to increase the understanding of road user behaviour in different European regions and in regular as well as (near-) crashes conditions. Moreover, it focuses on making road traffic safer and more sustainable by reducing fuel consumption and harmful emissions. The description and modelling of road user behaviour and specifically the effects of driving style, road network characteristics and traffic conditions is another objective of UDRIVE project. Last but not least, the UDRIVE project intents to provide data access to researchers from all over the world to assist with subsequent analyses regarding road safety (Eenink et al., 2014b; Barnard et al., 2016).