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Novice drivers are at high risk for crash involvement. We performed an analysis of causations, injury patterns and distributions of novice drivers in cars and on motorcycles in road traffic as a basis for proper measurements. Method Data of accident and hospital records of novice drivers (licence < 2 years) were analysed focusing the following parameters: injury type, localisation and mechanism, Abbreviated Injury Scale (AIS), maximum AIS (MAIS), delta-v, collision speed and other technical parameters and have been compared to those of experienced drivers. In 18352 accidents in the area of Hannover (years1985"2004), 2602 novice drivers and 18214 experienced drivers were recorded having an accident. Novice car drivers were more often and severe injured than experienced and on motorcycles the experienced riders were at higher risk. Novice drivers of both groups sustained more often extremity injuries. 4.5 % novice car drivers were not restraint compared to 3.7 % of the experienced drivers and 6.1 % novice motorcycle drivers did not wear a proper helmet (versus 6.5 %). Severe injuries sustained at a rate of 20 % at collision speeds below 30 km/h and in 80% at collision speeds above 50 km/h. Novice car drivers drove significant older cars. The risk profile of novice drivers is similar to those of drivers older than 65 years. Structural protection and special lectures like skidding courses could be proper remedial action next to harder punishment of violations.
A lot of factors are related to a road traffic accident; particularly human factors such as road use characteristic, driving maneuver characteristic and safety attitude are the major ones. As a random factor is also included, so it is necessary to minimize the contribution of a random factor to identify human factors related to a road traffic accident. There are several standpoints for traffic accident analysis, such as vehicle-based, location-based and driver-based. And it is effective to analyze driver-based traffic accident data for discussion on the relation between human factors and accidents. An integrated traffic accident database system was developed for analysis considering driver- accident and violation records by ITARD, and several studies were carried out for the evaluation. Useful data for discussion on the relation between types of collision and traffic violations, and the effect of accident experience to the following accident were obtained.
Intelligente Bauwerke - Anforderungen an die Aufbereitung von Messgrößen und ihrer Darstellungsform
(2015)
Ziel des Forschungsvorhabens FE 15.0548/2011/GRB war die Ausarbeitung einer Konzeption zu Anforderungen an die Aufbereitung und Verarbeitung von Messgrößen und ihrer Darstellung. Dies beinhaltete die Evaluierung und Entwicklung verschiedener modellbasierter und statistischer Analyseverfahren, die über den bisherigen Stand der Technik der Bauwerksüberwachung hinausgehen. Der Nutzen liegt in der Herausfilterung relevanter Informationen aus umfangreichem Datenmaterial (Vor-Aggregation) sowie der Erzeugung von belastbaren Zustandsinformationen bzw. Vorergebnissen hierzu durch eine frühzeitige, leistungsfähige Plausibilisierung. Es wurde gezeigt, dass durch den Einsatz von leistungsfähigen, gedächtnisbehafteten, selbstlernenden Algorithmen für die Sensorfusion, Interpolation, Plausibilitätserhöhung und Treffen fachtechnischer Monitoringaussagen Ergebnisse erzielt werden können, die bezüglich der Verarbeitung von Mess- und Erfassungsdaten weit über den aktuellen Stand beim Brückenmonitoring hinausgehen. Anhand mehrerer Szenarien, für die reale Erfassungsdaten zur Verfügung standen, wurde gezeigt, dass diese Verfahren sehr zuverlässig verschiedenste Signalstörungen, wie Messausreisser, erhöhtes Rauschen und Brummeinstreuung erkennen können. Nur durch die frühzeitige und zuverlässige Plausibilisierung von Sensordaten von Brückenbauwerken kann verhindert werden, dass offensichtlich fehlerhafte Messwerte (wie z.B. Messausreisser, störungsbehaftete Messungen) zu falschen Vorhersagen der Systemzuverlässigkeit von Brückenbauwerken durch rechnergestützte Systemmodelle führen.
Interdisciplinary accident research and research projects of AARU Audi Accident Research Unit
(2017)
AARU (Audi Accident Research Unit) is an interdisciplinary research project of the University Hospital Regensburg in cooperation with AUDI AG. Specific objective is to comprehend the respective accident scenario and retrieve generally applicable findings as to technical, medical and psychological processes. In order to prevent traffic accidents and to alleviate vehicle accident consequences, postulates of general traffic safety, human-machine interaction, technical design and function of new vehicles and occupant as well as third party protection shall be inferred from these findings. Specifically, each accident with new Audi, Lamborghini and Ducati vehicles involved is analyzed interdisciplinary, discussed in a case meeting and anonymously documented with more than 2,000 parameters. The database is continually used for solving safety relevant issues. Parallel to accident analysis, research projects are performed in the fields medicine, psychology and engineering in order to gain comprehensive insight and identify potential additional areas of activity of accident research.
Ziel der Arbeit war die Aufstellung von Maßnahmen, die zur Erhöhung der nächtlichen Verkehrssicherheit auf außerörtlichen Straßen (ohne BAB) geeignet erscheinen. Die Maßnahmen wurden bezüglich ihrer Wirksamkeit bewertet, ihre Durchsetzbarkeit abgeschätzt. Einer detaillierten Auswertung der relevanten Unfallstatistik folgen die Aufstellung und Beschreibung spezifischer Maßnahmen. Diese werden bewertet und einer Prioritätenreihung unterzogen. Umsetzungsmöglichkeiten werden ausführlich diskutiert. Die aus der Untersuchung abgeleiteten Empfehlungen betreffen in erster Linie verstärkte Informationsarbeit und die Beseitigung von Unfallschwerpunkten.
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.
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.
Teil 1: In der Verkehrssicherheitsforschung können Sicherheitswirkungen zumeist nicht im (Labor)experiment sondern nur durch Feldversuche, die einem Experiment ähnlich anzulegen sind, erfasst werden. In diesem Zusammenhang spricht man von einem Quasi-Experimentellen-Design. Da sich bei solchen Quasi-Experimenten immer die Frage stellt, ob die gemessenen Änderungen maßnahmebedingt sind oder auf andere Einflüsse zurückgeführt werden können, werden verschiedene Gefahrenquellen benannt und mit Beispielen belegt, die die Gültigkeit der Ergebnisse von Wirksamkeitsuntersuchungen beeinträchtigen können. Es werden darüber hinaus eine Reihe wichtiger Quasi-Experimenteller-Designs dargestellt, die Vor- und Nachteile der Designs diskutiert und Hinweise auf mögliche Einsatzfelder gegeben. Um die Qualität der Ergebnisse von Wirksamkeitsuntersuchungen zu verbessern, wird vorgeschlagen, mehrere Erhebungen mittels ein und derselben Untersuchungsanordnung zugleich an mehreren Orten oder an mehreren Untersuchungsgruppen durchzuführen. Es werden mehrere simultane Untersuchungsdesigns vorgestellt und Einsatzbereiche erläutert. Teil 2: Simultane Wirksamkeitsuntersuchungen von Maßnahmen zur Hebung der Verkehrssicherheit erfordern häufig statistische Methoden zur Analyse von Kennzahlen, die auf andere Kennzahlen bezogen sind. Diese bezogenen Kennzahlen bezeichnet man als Risikogrößen. Es werden Methoden vorgestellt und diskutiert, die eingesetzt werden können, wenn entweder die Bezugsgrößen fest oder stochastisch sind.
While the number of fatal accidents is diminishing every year, there is still a need of improvement and action to prevent these deaths. Basis for this purpose has to be an analysis about the factors influencing the car crash mortality. There are various studies describing the univariate influence of several factors, but crash scenarios are too complex to be described by a single variable. The multivariate analysis respects the interference of the variables and gets so to more detailed and representative results. This multivariate analysis is based on about 2,600 cases (the data have been collected by the accident research units Hannover and Dresden (during the years 1999-2003). This paper presents a multivariate model (containing ten different variables) which detects 93% of these cases properly. This means it detects the cases as truly survived and truly death.
Motorcycle crashes in Austria: Analysis of causes and contributing factors based on in-depth data
(2017)
From CEDATU, the in-depth accident database run by the Vehicle Safety Institute at Graz University of Technology, a representative sample of 101 crashes involving at least one motorcycle was selected. The analysis focused on causes for crashes as well as on contributing factors, but also included parameters of road, riders and vehicles. Own riding speed and "unexpectable action by another road user" were the most frequent causes for accidents. Inappropriate safety distance or delayed reaction were frequent, both as causation factors and as contributing factors. Infrastructure issues never cause an accident, but they are very frequent as contributing factors; road geometry and road guidance are by far most frequent among these. This paper also discusses accidents by type and other parameters (e.g. injury severity by body region, collision speed, age and others), and compares accident causes to previous studies as well as the police reported accident statistics.