81 Unfallstatistik
While it is important to track trends in the number of road accidents in different countries using national statistics, there is a need for data with more detailed information, so called in-depth accident data. For this reason, several accident data projects emerged worldwide in recent years. However, also different data standards were established and so comparative analysis of international in-depth data has been very hard to conduct, so far. This is why the project iGLAD (Initiative for the Global Harmonization of Accident Data) was established and created the prerequisites for building up a standardized dataset out of the common denominator of different in-depth accident databases from Europe, USA and Asia. In the first phase, the project received funding from ACEA to compile an initial database. To accomplish this, a suitable data scheme has been defined, a pilot study has been conducted as proof of concept and the recoding of the first common data base has been initiated. Also, to prepare the project for its self-supporting continuation in the next years, a business model has been developed. This paper reports the history and status of the project, the current challenges and the creation of a capable consortium to maintain the data. In mid-2014, the initial database containing 1550 cases from 10 different countries will be completed and a first detailed view on this data will be possible.
The Centre for Automotive Safety Research (formerly the Road Accident Research Unit) at the University of Adelaide in South Australia has a history of in-depth crash investigation going back to the 1970s. In recent years, our focus has been on studying factors that contribute to road crashes, with an emphasis on the role of road infrastructure. Our method involves crash notification by the South Australian Ambulance Service and detailed investigation of the crash scene usually before the crash-involved vehicles have been moved. This at-scene data collection is supplemented with police crash reports, Coroner- reports including autopsy findings for fatal crashes, case notes from hospitals for all injured persons, structured interviews with crash participants and witnesses, and computerised reconstruction of the events of the crash. One of the most notable research findings to emerge from our in-depth work has been the relationship between travelling speed and the risk of crash involvement. By comparing the calculated free speeds of crash-involved vehicles (cases) with the measured speeds of non-crash-involved vehicles travelling on the same roads at the same time of day (controls), we were able to establish that an exponential relationship exists between travelling speed and the likelihood of involvement in a casualty crash. This was the case for both metropolitan and rural areas. This research prompted the reduction of some speed limits in Australia, which has resulted in notable decreases in crash numbers. Another finding of interest in our recent investigation of 298 mostly daytime crashes in metropolitan Adelaide was that medical conditions make a sizeable contribution to the occurrence of road crashes. We found that almost half of the drivers, riders and pedestrians involved in the collisions had at least one pre-existing medical condition, and half of these individuals had two or more such conditions. We found that a medical condition was the direct causal factor in 13% of the casualty crashes investigated and accounted for 23% of all hospital admission or fatal crash outcomes. A follow-up study of all hospital admissions for road crashes in Adelaide is now going ahead to look further at this problem. The paper also describes studies looking specifically at pedestrian crashes. These include studies of the relationship between travelling speed and the risk of a fatal pedestrian crash, and studies utilising real crash data to validate headforms and test dummies used in the assessment of the safety of new vehicles in the event of a collision with a pedestrian.
Each year the traffic accident research teams in Dresden and Hanover provide an in-depth investigation of approximately two thousand accidents, aggregated in the GIDAS database. To accomplish a comprehensive review of each traffic accident recorded, a sensible and thorough encoding of suffered injuries is indispensable. The Abbreviated Injury Scale by AAAM offers a valuable and handy solution to achieve this goal. However, there were a few difficulties in the use of the AIS that came up in the past, which let to necessary improvements for the utilization of the AIS 2005 for GIDAS.
In der Bundesanstalt für Straßenwesen (BASt) wurde die "Datenbank internationaler Verkehrs- und Unfalldaten" (INVUD) eingerichtet, in der internationale Daten zum Verkehrs- und Unfallgeschehen sowie zu den Einwohnerzahlen und Fahrzeugbeständen gesammelt und DV-gestützt verarbeitet werden. Ein erster Bericht über diese INVUD-Datenbank wurde bereits Anfang 1987 als Heft 152 der Forschungsberichte der Bundesanstalt für Straßenwesen, Bereich Unfallforschung, veröffentlicht. Inzwischen ist der Ausbau der INVUD-Datenbank weit vorangeschritten. Seit Anfang 1988 besteht ein Kooperationsvertrag mit der Kommission der Europäischen Gemeinschaften (KEG), in dessen Rahmen unter anderem der Kreis der Länder, deren Daten gesammelt werden, auf alle EG-Länder erweitert worden ist. Zu den Vorteilen der Kooperation mit der KEG gehört auch deren wirkungsvolle Unterstützung bei der Beschaffung noch fehlender Daten. Für die Zukunft wird im Rahmen des OECD Road Transport Research Programme geplant, dass die hier beschriebene INVUD-Datenbank als Kern einer "IRTAD - International Road Transport and Accident Data Base" auf alle OECD-Länder ausgedehnt wird und zur internationalen Nutzung gelangt. Nachdem die bisherigen Ausbauabschnitte der INVUD-Datenbank weitgehend abgeschlossen sind, wird mit dem vorliegenden Bericht der Sachstand zu Beginn des Jahres 1989 dokumentiert und eine Informationsbasis für künftige Daten-Interessenten und Nutzer bereitgestellt. Vor diesem Hintergrund wird neben der Vorgehensweise bei der Datenbeschaffung, Aufbau und Administration der Datenbank sowie insbesondere der Datenbestand und der Zugriff auf die Daten beschrieben.
Die von verschiedenen internationalen Organisationen veröffentlichten Daten zur Verkehrssicherheit erweisen sich für die verkehrspolitischen Informationsbedürfnisse und für Zwecke der Unfallforschung häufig nicht als ausreichend. In Absprache mit dem Bundesministerium für Verkehr und der Bundesanstalt für Straßenwesen wurde daher eine Datenbank internationaler Verkehrs- und Unfalldaten aufgebaut. Berichtet wird über die Vorgehensweise bei der Datenbeschaffung und der Aufschlüsselung der Struktur der gespeicherten Daten. Für die Datenpflege und -fortschreibung ist in einer Quellendatenbank zu jeder abgespeicherten Zahl die zugehörige Quelle verzeichnet. Zum Aufbau der Datenbank wurde das Datenbanksystem SIR mit hierarchischem Aufbau verwendet. Nach dem Erreichen eines hinreichenden Ausbauzustandes soll die Datenbank allen Benutzern des Rechenzentrums BMV/BASt offenstehen.
Internationally, the need is expressed for harmonized traffic accident data collection (PSN, PENDANT, etc.). Together with this effort of harmonization, traffic accident investigation moves more and more in the direction of accident causation. As current methods only partly address these needs, a new method was set up. The main characteristics of this method are: • Accident/injury causation (associated) factors can objectively be identified and quantified, by comparison with exposure information from a normal population. • All relevant accident and exposure data can be included: human-, vehicle-, and environmental related data for the pre-crash, crash and postcrash situation (the so-called Haddon matrix). The level of detail can be chosen depending on interest and/or budget, which makes the method very flexible. In this paper the accident collection and control group method are presented, including some of the achieved results from a pilot study on 30 truck accidents and 30 control locations. The data were analyzed by using cross-tabulations and classification-tree analysis. The method proved useful for the identification of statistically significant causational aspects.
The SafetyNet project was formulated in part to address the need for safety oriented European road accident data. One of the main tasks included within the project was the development of a methodology for better understanding of accident causation together with the development of an associated database involving data obtained from on-scene or "nearly onscene" accident investigations. Information from these investigations was complemented by data from follow-up interviews with crash participants to determine critical events and contributory factors to the accident occurrence. A method for classification of accident contributing factors, known as DREAM 3.0, was developed and tested in conjunction with the SafetyNet activities. Collection of data and case analysis for some 1 000 individual crashes have recently been completed and inserted into the database and therefore aggregation analyses of the data are now being undertaken. This paper describes the methodology development, an overview of the database and the initial aggregation analyses.
A national initiative from the vehicle manufacturers, safety system suppliers, the road administration and universities in Sweden took off in 2007. The aim was to develop a national investigation network and a methodology focusing on all phases of a crash (pre-crash, in-crash and post-crash) as well as all parts of the road transport system (road user, vehicle and road environment). The initiative is formally run as a project with the acronym INTACT (Investigation Network and Accident Collection Techniques). It was a three year pilot with the aim to develop methodologies for an extended national crash investigation activity. During the first year the INTACT partners agreed on the aim for the investigation and methods for retrieving the data were developed. During the second and third year the methodology was tested in real-world investigations and further refinement was made. The paper describes the methodology developed to obtain high qualitative in-depth road crash data.
The accident research project in Dresden was founded in July 1999. To date over 6.000 crash investigations have been undertaken. About 10.000 vehicles have been documented and over 13.000 participants have been debriefed. But there is much more than this scientific success. Because of the interdisciplinary character between the medical and technical focus, the project affords an important contribution for the education of the involved students. Over 200 students of different fields of study have got experiences not only for the occupational career. This lecture describes the additional effects of the accident research project regarding the education of the students, the capacity for teamwork and learning about dealing with accident casualties.
Empirical vehicle crashworthiness studies are usually based on national or in-depth traffic accident surveys: Data on accident-involved cars/drivers are analysed in order to quantify the chance of driver injury and to assess certain risk factors like car make and model. As the cars/drivers involved in the same accident form a "cluster", where the size of the cluster equals the number of accident-involved parties, traffic accident survey data are typical multi-level data with accidents as first-level or primary and cars/drivers as secondlevel or secondary units (car occupants in general are to be considered as third level units). Consequently, appropriate statistical multi-level models are to be used for driver injury risk estimation purposes as these models properly account for the cluster structure of traffic accident survey data. In recent years various types of regression models for clustered data have been developed in the statistical sciences. This paper presents multi-level statistical models, which are generally applicable for vehicle crashworthiness assessment in the sense that data on single and multiple car crashes can be analysed simultaneously. As a special case of multi-level modelling driver injury risk estimation based on paired-by-collision car/driver data is considered. It is demonstrated that assessment results may be seriously biased, if the cluster structure inherent in traffic accident survey data is erroneously ignored in the data analysis stage.