81 Unfallstatistik
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A total survey of road traffic accidents involving most severely injured, defined as sustaining a polytrauma or severe monotrauma (ISS > 15) or being killed, was conducted over 14 months in a large study region in Germany. Data on injuries, pre-clinical and clinical care, crash circumstances and vehicle damage were obtained both prospectively and retrospectively from trauma centers, dispatch centers, police and fire departments. 149 patients with a polytrauma and eight with a severe monotrauma were recorded altogether. 22 patients died in hospital. Another 76 victims had deceased at the accident scene. In 2008, 49 % of patients treated with life-threatening injuries were car or van occupants, 21 % motorcyclists, 18 % cyclists and 10 % pedestrians. Among fatalities at the scene, vehicle occupants constituted an even larger portion. The number of road users with life-threatening trauma in the region was extrapolated to the German situation. It suggests that 10 % among the "seriously injured" as defined in national accident statistics are surviving accident victims with a polytrauma or severe monotrauma.
The National Highways Development Project in India is aimed at upgrading over 12,000 km of national highways from 2-lane undivided roads to 4-lane divided roads. With nearly 40% of fatal crashes being reported on national highways, the effect of this project on road safety needs to be assessed. Researchers carried out on-site crash investigations and in-depth crash data collection for a period of 45 to 60 days on four 2-lane undivided highways and a 4-lane divided highway. Based on 76 crashes examined, researchers found a shift of crash pattern from head-on collisions on undivided 2- lane highways to front-rear collisions on divided 4-lane highways. This paper presents the methodology, analysis of crashes examined, and the critical safety problems identified for greater consideration in future highway development projects. This paper also highlights the need and significance of in-depth crash investigations to understand local traffic conditions and problems in India.
The overall purpose of the ASSESS project is to develop a relevant and standardised set of test and assessment methods and associated tools for integrated vehicle safety systems, primarily focussing on currently available pre-crash sensing systems. The first stage of the project was to define casualty relevant accident scenarios so that the test scenarios will be developed based on accident scenarios which currently result in the greatest injury outcome, measured by a combination of casualty severity and casualty frequency. The first analysis stage was completed using data from a range of accident databases, including those which were nationally representative (STATS19, UK and STRADA, SE) and in-depth sources which provided more detailed parameters to characterise the accident scenarios (GIDAS, DE and OTS, UK). A common analysis method was developed in order to compare the data from these different sources, and while the data sets were not completely compatible, the majority of the data was aligned in such a way that allowed a useful comparison to be made. As the ASSESS project focuses on pre-crash sensing systems fitted to passenger cars, the data selected for the analysis was "injury accidents which involved at least one passenger car". The accident data analysis yielded the following ranked list of most relevant accident scenarios: Rank Accident scenario 1 Driving accident - single vehicle loss of control 2 Accidents in longitudinal traffic (same and opposite directions) 3 Accidents with turning vehicle(s) or crossing paths in junctions 4 Accidents involving pedestrians The ranked list highlights the relatively large role played by "accidents in longitudinal traffic", and "accidents with turning vehicle(s) or crossing paths in junctions" (the second and third most prevalent accident scenarios, respectively). The pre-crash systems addressed in ASSESS propose to yield beneficial safety outcomes with specific regard to these accident scenarios. This indicates that the ASSESS project is highly relevant to the current casualty crash problem. In the second stage of the analysis a selection of these accident scenarios were analysed further to define the accident parameters at a more detailed level .This paper describes the analysis approach and results from the first analysis stage.
This study that was funded by the Research Association for Automotive Technology (FAT) develops a method for the evaluation of the placement of tanks or batteries by using the deformation frequencies in real-world accidents. Therefore, the deformations of more than 20.000 passenger cars in the GIDAS database are analysed. For each vehicle a contour of deformation is calculated and the deformed areas of the vehicles are transferred in a rangy matrix of deformation. Thereby, the vehicle is divided into more than 190.000 cells. Afterwards, all single matrices of deformation are summarized for each cell which allows representative analyses of the deformation frequencies of accidents with passenger cars in Germany. On the basis of these deformation frequencies it is possible to determine least deformed areas of all passenger cars. Furthermore, intended placements of tanks or batteries can be estimated in an early stage of development. Therefore, all vehicles with deformations in the intended tank areas can be analysed individually. Considering numerous parameters out of the GIDAS database (e.g. collision speed, kind of accident, overlap, collision partner etc.) the occurring forces can be calculated or the deformation frequency can be estimated. Furthermore, it is possible to consider the influence of primary and secondary safety systems on the deformation behaviour. The analysis of "worst case accident events" is an additional application of the calculated matrix of deformation frequency.
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.
Ziel des Forschungsprojektes war die quantitative Vorausschätzung des Straßenverkehrsunfallgeschehens der Jahre 2015 und 2020 in Deutschland mit Hilfe eines eigens entwickelten Prognoseverfahrens. Das Verfahren sollte eine größtmögliche Differenzierung des zukünftigen Unfallgeschehens nach Schweregrad, Art der Verkehrsbeteiligung und Alter der Verkehrsteilnehmer erlauben. Das Modell sollte grundsätzlich in der Lage sein, Ursache - Wirkungszusammenhänge differenzierter als in herkömmlichen Ansätzen der Zeitreihenanalyse und deren Trendfortschreibung abzubilden. Den Prognosehorizont bilden die Jahre 2015 und 2020. Im Rahmen des vorliegenden Projekts erfolgte für Deutschland erstmals eine Prognose der Unfall- und Verunglücktenzahlen über eine Risikoanalyse maßgebender Unfallkonstellationen. Dabei wurde sowohl nach Ortslagen, Unfallbeteiligten und Alter der Verkehrsteilnehmer unterschieden. Mit Hilfe des vorgestellten Prognosemodells lässt sich der künftige Grad der Straßenverkehrssicherheit differenziert beurteilen. Auswirkungen der sich ändernden Rahmenbedingungen auf das Unfallgeschehen werden sowohl auf der Ebene der Unfallentstehung als auch auf der Ebene der Unfallschwere berücksichtigt. Dabei kann insbesondere der Einfluss aus Demografie und sich verändernder Zugangsvoraussetzungen zu Verkehrsmitteln auf das Unfallgeschehen abgebildet werden. Der vorgestellte erste Entwicklungsstand des Modells bietet daher bereits sehr gute Möglichkeiten, Wirkungsanalysen bei veränderten Einflussgrößen durchzuführen. Das Unfallprognosemodell wurde modular aufgebaut. Dadurch konnte eine logische und hierarchische Modellstruktur realisiert werden. In der Folge werden die einzelnen Module im Gesamtmodell sequentiell durchlaufen, sind in sich geschlossen und folgen eigenen Berechnungsvorschriften. Eine Umsetzung des Modells erfolgte auf Basis verknüpfter Excel-Dateien mit Hilfe von VBA-Makros. Hierbei wurde auf eine stark getrennte Struktur der einzelnen Berechnungsschritte Wert gelegt, um die einzelnen Dateien übersichtlich und nachvollziehbar zu gestalten. Gleichzeitig erfüllt das Modell die Forderung einer größtmöglichen Variabilität. So können sowohl geänderte Eingangsdaten zugrundegelegt werden als auch die Auswahl der differenzierten Trendberechnung beliebig getroffen werden. Im Ergebnis ist auf Basis der getroffenen Annahmen, der historischen Entwicklung und der konstellationenfeinen Fortschreibung der Risikofaktoren ein deutlicher Rückgang der Unfall- und Verunglücktenzahlen in Deutschland für den Prognosezeitraum gegenüber 2006 zu erwarten. Bei den Unfällen mit Personenschaden ist bis 2020 mit einer Abnahme um nahezu 30 % zu rechnen, bei den Verunglückten kann von einer Reduzierung um 13 % ausgegangen werden. Die Zahl getöteter Personen sinkt dabei voraussichtlich von ca. 5.100 Personen (2006) auf 2.700 Personen (2020). In Bezug auf die Schwerverletzten ist im gleichen Zeitraum mit einem Rückgang um ca. 33.000 Personen zu rechnen (2006: 74.500 Personen). Ebenso sinkt gegenüber dem Analysejahr 2006 die Anzahl Leichtverletzter um etwa 6 % auf etwa 326.000 Personen. Die Rückgänge der Verunglücktenzahlen liegen zwischen 2006 und 2015 sowie zwischen 2015 und 2020 zahlenmäßig auf einem vergleichbaren Niveau (55.000 bzw. 58.000 V). Somit wird etwa die Hälfte der Gesamtrückgänge im Prognosezeitraum allein in den letzten fünf Jahren der insgesamt fünfzehnjährigen Zeitspanne erreicht.