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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.
A reduction of around 48% of all road fatalities was achieved in Europe in the past years including a reduced number of fatalities with an older age. However, among all road fatalities, the proportion of elderly is steadily increasing. In an ageing society, the European (Horizon2020) project SENIORS aims to improve the safe mobility of older road users, who have different transportation habits compared to other age groups. To increase their level of safe mobility by determining appropriate requirements for vehicle safety systems, the characteristics of current road traffic collisions involving the elderly and the injuries that they sustain need to be understood in detail. Hereby, the paper focuses on their traffic participation as pedestrian, cyclist or passenger car occupant. Following a literature review, several national and international crash databases and hospital statistics have been analysed to determine the body regions most frequently and severely injured, specific injuries sustained and types of crashes involved, always comparing older road users (65 years and more) with mid-aged road users (25-64 years). The most important crash scenarios were highlighted. The data sources included European statistics from CARE, data on national level from Germany, Sweden, Italy, United Kingdom and Spain as well as in-depth crash information from GIDAS (Germany), RAIDS (UK), CIREN and NASS-CDS (US). In addition, familiar hospital data from Germany (TraumaRegister DGU-®), Italy (Italian Register of Acute Traumas) and UK hospital statistics (TARN) were included in the study to gain further insight into specific injury patterns. Comprehensive data analyses were performed showing injury patterns of older road users in crashes. When comparing with mid-aged road users, all databases showed that the thorax body region is of particularly high importance for the older car occupant with injury severities of AIS 2 or AIS 3+, whereas the body regions lower extremities, head and thorax need to be considered for the older pedestrians and cyclists. Besides these comparisons, the most frequent and severe top 5 injuries were highlighted per road user group. Further, the most important crash configurations were identified and injury risk functions are provided per age group and road user group. Although several databases have been analysed, the picture on the road safety situation of older road users in Europe was not complete, as only Western European data was available. The linkage between crash data and hospital data could only be made on a general level as their inclusion criteria were quite different.
While cyclists and pedestrians are known to be at significant risk for severe injuries when exposed to road traffic accidents (RTAs) involving trucks, little is known about RTA injury risk for truck drivers. The objective of this study is to analyze the injury severity in truck drivers following RTAs. Between 1999 and 2008 the Hannover Medical School Accident Research Unit prospectively documented 43,000 RTAs involving 582 trucks. Injury severity including the abbreviated injury scale (AIS) and the maximum abbreviated injury scale (MAIS) were analyzed. Technical parameters (e.g. delta-v, direction of impact), the location of accident, and its dependency on the road type were also taken into consideration. The results show that the safety of truck drivers is assured by their vehicles, the consequence being that the risk of becoming injured is likely to be low. However, the legs especially are at high risk for severe injuries during RTAs. This probability increases in the instance of a collision with another truck. Nevertheless, in RTAs involving trucks and regular passenger vehicles, the other party is in higher risk of injury.
To elucidate the risk of pedestrians, bicycle and motorbike users, data of two accident research units from 1999 to 2014 were analysed in regard to demographic data, collision details, preclinical and clinical data using SPSS. 14.295 injured vulnerable road users were included. 92 out of 3610 pedestrians ("P", 2.5%), 90 out of 8307 bicyclists ("B", 1.1%) and 115 out of 4094 motorcycle users ("M", 2.8%) were diagnosed with spinal fractures. Thoracic fractures were most frequent ahead of lumbar and cervical fractures. Car collisions were most frequent mechanism (68, 62 and 36%). MAIS was 3.8, 2.8 and 3.2 for P, B and A with ISS 32, 16 and 23. AIS-head was 2.2, 1.3 and 1.5). Vulnerable road users are at significant risk for spine fractures. These are often associated with severe additional injuries, e.g. the head and a very high overall trauma severity (polytrauma).
Since its creation in 2011 the Pre-Crash-Matrix (PCM) offers the possibility to observe the pre-crash phase until five seconds before crash for a wide range of accidents. Currently the PCM contains more than 8.000 reconstructed accidents out of the GIDAS (German In-Depth Accident Study) database and is enlarged continuously by more than 1.000 cases per year. Hence, a detailed investigation of active safety systems in real accident situations has been made feasible. The PCM contains all relevant data in database format to simulate the pre-crash phase until the first collision of the accident for a maximum of two participants. This includes the definition of the participants and their characteristics, the dynamic behavior of the participants as time-dependent course for five seconds before crash as well as the geometry of the traffic infrastructure. The digital sketch of the accident and information from GIDAS as well as from supplementary databases represent the main input for the simulation of the pre-crash phase of an accident with the VUFO simulation model VAST (Vufo Accident Simulation Tool). This simulation in turn embodies the foundation of the PCM. The PCM underlies continual improvements and enhancements in consultation with its users. In addition to collisions of cars with other cars, pedestrians, bicycles and motorcycles the PCM now also covers car to object and car to truck collisions. The paper illustrates car to truck collisions as a showcase and explains perspectives for further developments. In 2016 a more detailed definition of the contour of the vehicle was added. Furthermore, the geometrical surroundings of the accident site will be provided in a new structure with a higher level of detail. Thus, a precise classification of road marks and objects is possible to further improve the support of developing and evaluating ADAS. This paper gives an overview about the latest developments of the PCM with its innovations and provides an outlook to upcoming enhancements. Besides potential areas of application for the development of ADAS are shown.
The objectives of this paper are the analysis of the accident risk of drivers brain pathologies (Mild Cognitive Impairment, Alzheimer- disease, and Parkinson- disease), and the investigation of the impact of driver distraction on the accident risk of patients with brain pathologies, through a driving simulator experiment. The three groups of patients are compared to a healthy group of similar demographics, with no brain pathology. In particular, 125 drivers of more than 55 years old (34 "controls"" and 91 "patients") went through a large driving simulator experimental process, in which incidents were scheduled to occur. They drove in rural and urban areas, in low and high traffic volumes and in three distraction conditions (undistracted driving, conversation with a passenger and conversation through a mobile phone). The statistical analyses indicated several interesting findings; brain pathologies affect significantly accident risk and distraction affects more the groups of patients than the control one.
Ziel des Forschungsprojektes war die Erarbeitung eines webbasierten Verfahrens für die Verkehrssicherheitsarbeit, welches dem Anwender bei der Bearbeitung von Unfallhäufungen potenziell geeignete Maßnahmen in Abhängigkeit der örtlichen Randbedingungen vorschlägt, deren Sicherheitswirkung abschätzt sowie die Möglichkeit bietet, die Maßnahmenwirkung in einer retroperspektiven Betrachtung zu evaluieren. Dabei stellt das Verfahren eine Weiterentwicklung und Ergänzung des Merkblatts "Auswertung von Straßenverkehrsunfällen, Teil 2: Maßnahmen gegen Unfallhäufungen" (FGSV 2002) dar. Grundgerüst der Maßnahmensammlung bilden neben dem Merkblatt der FGSV (2002) aktuelle Erkenntnisse verschiedener Forschungsarbeiten (SPAHN 2012, GERLACH et al. 2009, MAIER et al. 2010, u. w.) zu Maßnahmen gegen Unfallhäufungen, die einer Prüfung und Kategorisierung unterzogen wurden. Der Hauptbestandteil des webbasierten Verfahrens umfasst Schritte zur Unfallanalyse, Maßnahmenfindung und Wirksamkeitsprüfung nach dem "Merkblatt zur örtlichen Unfalluntersuchung in Unfallkommissionen " M UKo" (FGSV 2012). Mit der Übermittlung der Unfallinformationen zu Unfallhäufungen aus den EDV-Systemen der Unfalldatenhaltung in das Programm ist eine spezifische Bearbeitung von Unfallhäufungen möglich. Darüber hinaus wird die Möglichkeit von Rangfolgebildungen zur zielgerichteten Priorisierung von Arbeitsprogrammen angeboten. Die Vorschläge geeigneter Maßnahmen zur Bekämpfung einer Unfallhäufung stuetzen sich im Verfahren auf die Analyse typischer Konfliktsituationen, welche aus den Unfalldatensätzen bestimmt werden. Zur Überprüfung der Angemessenheit und Durchsetzbarkeit von Maßnahmen (-paketen) steht dem Anwender eine Abschätzung des Nutzen-Kosten-Verhältnisses auf Basis des fallbezogenen Unfallgeschehens zur Verfügung. Die kontinuierliche Anwendung des Verfahrens erlaubt dem Nutzer die Dokumentation der Arbeitsschritte. Diese beinhaltet über die Umsetzungskontrolle hinaus wiederum eine fallbezogene Wirksamkeitsprüfung (Evaluierung) der realisierten Maßnahmen. Die stetige Aktualisierung der Maßnahmen und ihrer Kenngrößen (u. a. Wirkungsgrad, Kosten) stellt einen wesentlichen Bestandteil des webbasierten Verfahrens dar, um einen zielorientierten Beitrag zur Bekämpfung von Unfallhäufungen zu leisten.
Die Bundesanstalt für Straßenwesen (BASt) bringt zum Ende jeden Jahres eine Prognose der Unfall- und Verunglücktenzahlen des noch laufenden Jahres heraus, um so über die Entwicklung der Verkehrssicherheit in Deutschland Bilanz ziehen zu können. Dabei wird das Unfallgeschehen nach dem Schweregrad der Konsequenzen, der Ortslage sowie Alter und Art der Verkehrsbeteiligung der Verunglückten in 27 Zeitreihen unterteilt. Zu diesem Zeitpunkt sind die Daten lediglich für die ersten acht oder neun Monate erhältlich. Um Bilanz zu ziehen, werden die Anzahlen der letzten drei oder vier Monate prognostiziert. Gesamtziel des hier beschriebenen Forschungsvorhabens ist die Optimierung der jährlichen Unfallprognosen durch Anwendung von strukturellen Zeitreihenmodellen, bei denen die Vorhersagen aus dem Trend der vorliegenden Monate, und der Dynamik der vorhergehenden Jahre abgeleitet werden. Um dem Einfluss der Witterungsverhältnisse Rechnung zu tragen, werden dabei meteorologische Variablen in das Vorhersagemodell aufgenommen. Um die Modelle zu testen, werden die endgültigen Daten der letzten 15 Jahre jeweils aus den vorläufigen Daten der ersten Monate vorhergesagt und mit den tatsächlich beobachteten endgültigen Unfall- und Verunglücktenzahlen verglichen. Die Resultate zeigen, dass im Vergleich zu den bisherigen Vorhersagen mithilfe der hier vorgestellten Modelle die Vorhersagen für 25 der 27 Reihen präziser werden. Lediglich zwei Reihen zeigen einen leichten Anstieg des Vorhersagefehlers. Beim Vergleich von Modellen mit und ohne meteorologischen Variablen zeigt sich, dass 23 der 27 Reihen besser vorhergesagt werden können, wenn man das Wetter berücksichtigt. Neben der verbesserten Vorhersage ermöglicht die Aufnahme der Wettervariablen auch eine Einschätzung, wie groß der Einfluss der Witterungsgegebenheiten auf das Unfallgeschehen ist. Es zeigt sich also, dass die Anwendung von strukturellen Zeitreihenmodellen und die Berücksichtigung von meteorologischen Variablen zu einer deutlichen Verbesserung der Vorhersagegenauigkeit führen. Die Verbesserung der Vorhersagen durch die Aufnahme von Wettervariablen bestätigt nochmals den Einfluss der Witterungsumstände auf das Unfallgeschehen.