81 Unfallstatistik
Filtern
Erscheinungsjahr
- 2010 (25) (entfernen)
Dokumenttyp
Schlagworte
- Accident (18)
- Conference (18)
- Konferenz (18)
- Unfall (17)
- Injury (10)
- Verletzung (10)
- Fatality (9)
- Statistik (9)
- Tödlicher Unfall (9)
- Deutschland (8)
Institut
Accidents with vulnerable road users require special attention within the road safety work because these accidents are often accompanied with severe injuries. Thus In 2006 at least 6200 Powered Two Wheeler (PTW) riders were killed in road crashes in the EU 25 representing 16% of the total number of road deaths while accounting for only 2% of the total kilometers driven. For the prevention of accidents with VRU above all the knowledge of the causes of the accidents is of special importance. This study is based on the methodology of the German In-Depth Accident Study GIDAS. Within GIDAS extensive data on various fields of accidentology are collected on-scene from road traffic accidents with injuries in the Hannover and Dresden area. Using a well defined sample plan the collected data is highly representative to the whole German situation (Brühning et al, Otte et al). The need of in-depth accident causation data in accident research led to the development of a special tool for the collection of such data called ACASS (Accident Causation Analysis with Seven Steps), which was implemented in the GIDAS methodology in 2008 and described by Otte in 2009.
Small overlap frontal crashes are defined by a damage pattern with most of the vehicle deformation concentrated outboard of the main longitudinal structures. These crashes are prominent among frontal crashes resulting in serious and fatal injuries, even among vehicles that perform well in regulatory and consumer information crash tests. One of the critical aspects of understanding these crashes is knowing the crash speeds that cause the types of damage associated with serious injuries. Laboratory crash tests were conducted using 12 vehicles in three small overlap test conditions: pole, vehicle-to-vehicle collinear, and vehicle-to-vehicle oblique (15-degree striking angle). Field reconstruction techniques were used to estimate the delta V for each vehicle, and these results were compared with actual delta V values based on vehicle accelerometer data. Estimated delta Vs were 50% lower than actual values. Velocity change estimates for small overlap frontal crashes in databases such as NASS-CDS significantly underestimate actual values.
Causation patterns and data collection blind spots for fatal intersection accidents in Norway
(2010)
Norwegian fatal intersection accidents from the years 2005-2007 were analysed to identify any causation patterns among their underlying contributing factors, and also to evaluate whether the data collection and documentation procedures used by the Norwegian in-depth investigation teams produces the information necessary to perform causation pattern analysis. A total of 28 fatal accidents were analysed. Details on crash contributing factors for each driver in each crash were first coded using the Driving Reliability and Error Analysis Method (DREAM), and then aggregated based on whether the driver was going straight or turning. Analysis results indicate that turning drivers to a large extent are faced with perception difficulties and unexpected behaviour from the primary conflict vehicle, while at the same time trying to negotiate a demanding traffic situation. Drivers going straight on the other hand have less perception difficulties. Instead, their main problem is that they largely expect turning drivers to yield. When this assumption is violated, they are either slow to react or do not react at all. Contributing factors often pointed to in literature, e.g. high speed, drugs and/or alcohol and inadequate driver training, played a role in 12 of 28 accidents. While this confirms their prevalence, it also indicates that most drivers end up in these situations due to combinations of less auspicious contributing factors. In terms of data collection and documentation, information on blunt end factors (those more distant in time/space, yet important for the development of events) was more limited than information on sharp end factors (those close in time/space to the crash). A possible explanation is that analysts may view some blunt end factors as event circumstances rather than contributing factors in themselves, and therefore do not report them. There was also an asymmetry in terms of reported obstructions to view due to signposts and vegetation. While frequently reported as contributing for turning drivers, they were rarely reported as contributing for their counterparts in the same accidents. This probably reflects an involuntary focus of the analyst on identifying contributing factors for the driver legally held liable, while less attention is paid to the driver judged not at fault. Since who to blame often is irrelevant from a countermeasure development point of view, this underlying investigator mindset needs addressing to avoid future bias in crash investigation reports.
Recent findings from real-world accident data have shown that fatality risks for pedestrians are substantially lower than generally reported in the traffic safety literature. One of the keys to this insight has been the large and random sample of car-to-pedestrian crashes available in the German In-Depth Accident Study (GIDAS). Another key factor has been the proper use of weight factors in order to adjust for outcome-based sampling bias in the accident data. However, a third factor, a priori of unknown importance, has not yet been properly analysed. This is the influence of errors in impact speed estimation. In this study, we derived a statistical model of the impact speed errors for pedestrian accidents present in the GIDAS database. The error model was then applied to investigate the effect of the estimation error on the pedestrian fatality risk as a function of car impact speed. To this end, we applied a method known as the SIMulation-EXtrapolation (SIMEX) method. It was found that the risk curve is fairly tolerant to some amount of random measurement error, but that it does become flattened. It is therefore important that the accident investigations and reconstructions are of high quality to assure that systematic errors are minimised and that the random errors are under control.
The NHTSA-sponsored Crash Injury Research and Engineering Network (CIREN) has collected and analyzed crash, vehicle damage, and detailed injury data from over 4000 case occupants who were patients admitted to Level-I trauma centers following involvement in motor vehicle crashes. Since 2005, CIREN has used a methodology known as "BioTab" to analyze and document the causes of injuries resulting from passenger vehicle crashes. BioTab was developed to provide a complete evidenced-based method to describe and document injury causation from in-depth crash investigations with confidence levels assigned to the causes of injury based on the available evidence. This paper describes how the BioTab method is being used in CIREN to leverage the data collected from in-depth crash investigations, and particularly the detailed injury data available in CIREN, to develop evidence-based assessments of injury causation. CIREN case examples are provided to demonstrate the ability of the BioTab method to improve real-world crash/injury data assessment.