Research Question/Objective:​ The ageing society increases the requirements to focus on safe mobility for elderlies. It is expected that in Germany the population with an age above 65 years will reach more than 30% of the total population by 2060. In the same time the number of people above 80 years will double. In addition the relative share of driving license holder amongst the elderly will increase. In order to maintain the active role in mobility it is essential to identify specific risk factors of elderly and to develop countermeasures. The objective of this paper is to analyse specific accidents causes of elderly car drivers and to assess different measures such as improvement of the infrastructure, training measures, driver assistance systems etc.

Methods and Data Sources:​ For this study accident data of the Accident Research Unit Hannover (part of the GIDAS data sample) were used. The analysis focuses on the detailed 3 digit accident type and the Accident Causation Analysis System (ACAS) to identify functional problems in traffic situations with high accident risk for elderly. The driving task is derived from the detailed accident type, which describes the conflict that caused the accident and in more detail the positions and intended directions of the opponents.

ACAS as a hierarchic classification system and a sequence model is based on an in-depth data collection of predominantly directly event-related causation factors which were crucial in the accident emergence as situational resulting events and influences. The paradigm underlying this method refers to the findings of the psychological traffic accident research that most causally relevant features of the system components human, infrastructure and vehicle technology are found directly in the situation shortly before the accident. The focus in the immediate pre-crashphase lies on the human failures which are classified into five categories of basic human functions which are necessary to perform the driving task. With the detailed knowledge of the causes of the accident the causation factors are further specified into criteria of the categories and indicators of these criteria.

Results:​ The analyzed data shows that there are considerable age related deficits in the assessment of multiple information, e.g., reorientation after entering a crossing, observation of road users approaching from bypasses (e.g., cyclists) etc. Most of these deficits can be compensated by improved infrastructure, specific training modules and driver assistance systems. Predominantly information systems and active assistance systems for elderly drivers with the driving task “turning in intersections” can be useful.

Discussion and Limitations:​ Due to a limited number of elderly car drivers with ACAS codes in the GIDAS data set for most of the scenarios the number of cases is too low to analyse the data without grouping similar accident types and using main categories of the ACAS code. However, the data is consistent with knowledge from literature.

Conclusion and Relevance to session submitted:​ The paper presents data that judges which safety measures (mainly focusing on driver assistance systems) are beneficial for elderly car drivers on the one hand and describes relevant scenarios for the assessment of these safety measures on the other hand.