5th International Conference on ESAR
There is a need to continue to set the right vehicle safety policy priorities in the future. Research has to point out the most cost efficient and safety relevant measures to further reduce the number of road traffic casualties. The overall development shows that the constant and rapid decrease in the number of road casualties slows down. New innovations need to enter the vehicle market soon, in order to continue the success achieved in the last decade. Priorities for vehicle safety are driven by safety and mobility demands. It is necessary to keep a strong lid on all aspects of elderly and vulnerable road users. The fraction of powered-two-wheelers (PTW) is a priority group. PTWs have a risk of being involved in an accident, 14times higher than that of a passenger car. However, the figures do also show that every second fatality is a car occupant. Therefore passenger car safety remains to be top priority. Heavy goods vehicles are overly represented in fatal accidents, addressing the need to make these vehicles more compatible with other road users. These facts highlight the necessity not only to increase vehicles" self protection, but also to make cars - and trucks - more compatible and safe. Cycling is a strongly increasing mode of transport. This is a further reason to demand better protection for cyclists and pedestrians from car design and car active and integrated safety systems. Another priority for future vehicle safety is related to demographics. It is less known that the purely demographic effect will be superimposed by an increasing wish of elderly people to be mobile. However, elderly people show deficits concerning their biomechanics. This emphasizes the need for better and more adaptive restraint systems, but also further technological challenges and demands for active safety systems. However, in order to progress, current technological limitations have to be overcome. Cost benefit considerations, but also consumer acceptance and desires, will drive this process.
Police records about traffic accidents like used by IRTAD (International Road Traffic and Accident Database) and CARE (Community Road Accident Database) do not represent all road injuries. For instance, road accidents of bicyclists without a counterpart are usually not reported. Furthermore, IRTAD-like data contains hardly any information on injury outcome and accident circumstances. This information gap leads to an under-representation of the safety concerns of the most vulnerable road users like children and the elderly both in accident research and safety promotion. Injury registration for the European Injury Database (IDB), in turn, combines details of accident causation with diagnostic information that can be used to assess injury severity and long term consequences. The IDB is collecting data from hospital emergency department patients and is being implemented in a growing number of countries. In this article IDB results on mode of transport and injury outcome are presented from a sample of nine EU member states.
With an ever rising human life expectancy the share of elderly people in society is constantly rising. This leads to the fact that at the same rate the share of people with age related diseases such as dementia and poor eyesight taking part in traffic will rise and therefore traffic accidents caused by this group of people due to the disease will play an ever greater role. This Situation will be among the future challenges of road safety work. At present this study displays specific characteristics of accidents caused by elderly car drivers (aged 65 or higher) based on the analysis of the German In-Depth Accident Study GIDAS. Herein almost 1000 elderly car drivers were identified as accident participants in the years 2008 to 2011. The focus of this study lies on identifying special types of accidents which are caused by elderly drivers and on characterizing these types with the information gathered on scene and by interviewing the participants. The main evidence analyzed is the knowledge about the accident locality, the trajectories of the participants as well as the reasons for the occurrence of the accidents. Furthermore personal information such as the personal condition before the accident and driving purposes is used to identify patterns of contributing circumstances for accidents caused by elderly traffic participants.
From literature well-known analyzes on risks, hazards and causes of accidents of older drivers are amended by the present study in which a comparison of the specific features of accident causes of older car drivers (older than 60 years) and of younger car drivers (under 25 years) is conducted. Mainly the question is pursued if specific errors, mistakes and lapses are predominant in the two different age groups. The analysis system ACAS (Accident Causation Analysis System) used hereby consists of a sequential system of accident causation factors from the human, the technical and the infrastructural field, whereupon for this study the influence of the human features on the accident development in two different age groups is of interest. ACAS is both an accident model and an analysis and classification system, which describes the human participation factors of an accident and their causes in the temporal sequence (from the perceptibility to concrete action errors) taking into consideration the logical sequence of individual basic functions. In five steps (categories) of a logical and temporal sequence the hierarchical system makes human functions and processes as determinants of accident causes identifiable. The methodology specifically focuses on the use in so-called "In-Depth" and "On-Scene" investigation studies. With the help of the system for each accident participant one or more of five hypotheses of human cause factors are formed and then specified by appropriate verification criteria. These hypotheses in turn are further specified by indicators in such manner that the coding of the causation factors by a code system meets the needs of database processing and are accessible to a quantitative data analysis. The first results of the descriptive comparison of the two age groups concern mainly differences in the functional levels "information admission/perception" (where the elderly drivers have more difficulties than the young ones) and "information processing/evaluation" (where the younger drivers show more problems). Concerning the cognitive function of "planning" the group of younger drivers seems to be more often involved in an accident because of excessive speed.