Road Safety Data, Collection, Transfer and Analysis DaCoTa. Workpackage 6, Driver Behaviour Monitoring through Naturalistic Driving: Deliverable 6.4: Naturalistic Driving for monitoring safety performance indicators and exposure: considerations for implementation.
20151051 ST [electronic version only]
Schagen, I.N.L.G. van & Reed, S.
Brussels, European Commission, Directorate General for Mobility and Transport, 2012, 38 p., 16 ref.; Grant Agreement Number TREN/FP7/TR/233659 /"DaCoTA"
|Samenvatting||DaCoTA was a Collaborative Project under the European Seventh Framework Programme that aimed to develop tools and methodologies to support road safety policy and further extend and enhance the European Road Safety Observatory (ERSO). One of the Work Packages in DaCoTA, WP6, focused on the usefulness and feasibility of applying the Naturalistic Driving method for collecting comparable information about the road safety level in EU Member States and its development over time. The current Deliverable was prepared in this framework and gives an overview of the aspects to be taken into account when implementing ND research for monitoring purposes. Naturalistic Driving (ND) Naturalistic Driving (ND) can be defined as “A study undertaken to provide insight into driver behaviour during every day trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control”. Typically, in an ND study passenger cars, preferably the subjects' own cars, are equipped with several small cameras and sensors. These devices continuously and inconspicuously register vehicle manoeuvres (like speed, acceleration/deceleration, direction, location), driver behaviour (like eye, head and hand manoeuvres), and external conditions (like road, traffic and weather characteristics). ND for monitoring purposes ND data can, among other things, be used to establish how often drivers routinely are exposed to or engaged in certain situations/behaviours that are known to increase the risk of a crash. This includes monitoring safety-relevant behaviour (Safety Performance Indicators - SPIs) and mobility (Risk Exposure Data — RED). An important reason for monitoring road safety and comparing road safety levels and their developments over time in different countries is benchmarking. It allows countries to determine their relative position in comparison to other selected countries, to understand differences and find ways and get motivated to improve their position. Obviously, monitoring road safety also allow countries to evaluate their own road safety policy and road safety targets. ND is considered a promising approach for collecting reliable and comparable information about various RED and SPIs, as well as several relevant context variables. The main advantage of the ND approach for monitoring purposes as compared to the more traditional SPI data collection methods, such as road-side surveys and questionnaires, is that ND ensures continuous, automatic and standardized data collection. Provided that similar data acquisition systems and methods are applied in all participating cars, this approach substantially increases international comparability and level of detail. Though the current Deliverable is purely focused on road safety and exposure data, the collected data will also be useful for other transport areas, in particular eco-driving and traffic management. Three data collection scenarios Depending on the variable of interest, ND data collection needs different technologies ranging from simple and relatively cheap data acquisition systems to more sophisticated systems with several sensors as well as several videos covering the inside of the car and various directions outside the car. By combining the RED and SPIs of interest and the technological requirements for collecting that type of data, we distinguish three scenarios to collect meaningful data within reasonable limits of cost and complexity. It is recommended to start off with Scenario 1: a low-cost simple, off-the-shelf simple data acquisition system (e.g. an OBD GPS tracker or a Smart Phone) and a limited number of additional sensors, measuring: * Vehicle kilometres * Person kilometres * Number of trips * Time in traffic * Speed * Seat belt use * Light use In a later stage, additional SPIs and network characteristics could be added successively (Scenario 2), including: * Time headway * Acceleration * Lane departures * Inappropriate speed * Signal use * Junction type SPIs that would need continuous external and/or internal video recordings do not seem to be feasible in the short term, because this results in huge amounts of data and very high costs for the related data transfer and data coding. That means that the SPIs like fatigue, inattention, distraction and the (proper) use of child restraints can currently not be monitored by means of ND research. Technical developments may allow reconsideration of this conclusion in due time. Furthermore, it is recommended to equip a limited number of cars also with an event-triggered video in order to monitor numbers of near crashes as yet another relevant SPI (Scenario 3). As a very useful side product, this effort will provide data that can be used to further specify and refine the quantitative and qualitative relationship between near crashes and real crashes. Study design and organisational issues In principle, the techniques and procedures for ND data collection, data transfer, data storage and data analysis are available and not too complicated. In order to get reliable information, a fairly large sample is needed. The exact size of the sample depends on the variation in behaviour in the population and the required level of precision of the results. Assuming that the sample is drawn in a cleverly stratified way, resulting in a number of mutually exclusive and homogeneous subgroups (e.g. based on gender and age), a sample of around 10,000 drivers per country seems to be required for RED such as the annual amount of vehicle kilometres. This number is usually independent of the size of the population of car drivers in a country. Only if the sample size is larger than 10% of the population, a correction is applicable. It is recommended to collect data throughout the year on a continuous basis and to follow each individual in the sample for one year, applying a rotating scheme of 50% per 6 months. Analyses are best performed at a national level, applying a series of definitions on variables and disaggregation levels and following fixed analysis protocols. It could be considered to identify a limited number of ‘core’ variables (SPIs/RED) to be analysed at a central/ERSO level to ensure exact comparability. Participation in ND research is per definition on a voluntary basis and experiences in the USA and Europe have shown that it is requires special attention to find sufficient suitable participants, especially if there are strict sample stratification requirements. In addition, there are legal and ethical issues involved in ND research, in particular in the area of privacy and data protection. Exploring Scenario 4 In parallel to the implementation of the previous three scenarios, it is recommended to start exploring the possibility of a Scenario 4 now, i.e. a scenario where relevant data is extracted directly from the vehicle via CAN-bus, OBD, and other trip and travel data collected automatically by the vehicle. In theory, that way a lot of relevant information is already available with no or little additional costs; in practice, however, the information is not generally accessible nor comparable between car makes and models. So, this is a scenario that cannot be realised overnight. One of the first steps, in consultation with the car manufacturers, is an elaboration of the requirements for this data: what is available, what is needed, what is technically feasible. A central role for the EC Since harmonisation and international comparability of data are the key reason for this effort, there is a central role for the European Commission in initiating this task and taking the lead from here, most likely within the ERSO framework. A stepwise approach is recommended, including successively: 1. Creating support and finding budget by presenting the case to the relevant road safety bodies at European and Member State level, explaining the need for harmonised, comparable international data, the ND approach, and its added value. 2. Preparing a detailed description of / handbook for all practical implementation aspects, including the functional specifications of data collection equipment, participant selection, data transfer and storage, as well as definitions of variables, disaggregation levels and analyses. 3. Identifying the relevant national organisations which will be responsible for national data collection and pre-analyses, and fine-tuning data collection procedures (including legal aspects) and variable definitions in consultation with them. 4. Developing and equipping a database at EU level and defining the required (pre-analysed aggregated) data to be provided by the Member States as well as the procedures and time schedule, in consultation with the relevant national organisations. 5. Setting up European-wide communication strategies to guarantee maximum dissemination and use of the collected data. 6. Setting up one year national pilots in at least four Member States, well spread of Europe (North, West, South, East). 7. Adapting procedures and definitions, based on the pilot experiences. 8. Successive implementation of Scenario 1 in additional Member States. Parallel to steps 6 and 7, Scenario 2 (additional SPIs/RED) and 3 (monitoring nearcrashes) can be elaborated, piloted and implemented, applying a similar stepwise process. From the very beginning, the EC is advised to initiate discussions with the car manufacturers, using existing discussion platforms, with the aim to explore longer term possibilities of Scenario 4, i.e. the scenario where relevant data is extracted directly from the vehicle. (Author/publisher)|
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