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Side impacts, both nearside and farside, have been indicated by research to be responsible for a large proportion of serious injuries from road crashes. This study aimed to compare and contrast the characteristics of nearside and farside crashes in Australia, Germany and the U.S., using the ANCIS, GIDAS and NASS/CDS in-depth-databases, in order to establish the impact and injury severity associated with these crashes, and the types of injuries sustained. The analyses revealed some interesting similarities, as well as differences, between both nearside and farside crashes, and the emergent trends between the three investigated countries. More specifically, it was indicated that whilst the severity of injury sustained in nearside crashes was slightly greater overall than that found for farside crashes, careful consideration of struck and nonstruck side occupants must be made when considering aspects such as vehicle design and occupant protection.
Pedestrian and cyclist are the most vulnerable road users in traffic crashes. One important aspect of this study was the comparable analysis of the exact impact configuration and the resulting injury patterns of pedestrians and cyclists in view of epidemiology. The secondary aim was assessment of head injury risks and kinematics of adult pedestrian and cyclists in primary and secondary impacts and to correlate the injuries related to physical parameters like HIC value, 3ms linear acceleration, and discuss the technical parameter with injuries observed in real-world accidents based documented real accidents of GIDAS and explains the head injuries by simulated load and impact conditions based on PC-Crash and MADYMO. A subsample of n=402 pedestrians and n=940 bicyclists from GIDAS database, Germany was used for preselection, from which 22 pedestrian and 18 cyclist accidents were selected for reconstruction by initially using PC-Crash to calculate impact conditions, such as vehicle impact velocity, vehicle kinematic sequence and throw out distance. The impact conditions then were employed to identify the initial conditions in simulation of MADYMO reconstruction. The results show that cyclists always suffer lower injury outcomes for the same accident severity. Differences in HIC, head relative impact velocity, 3ms linear contiguous acceleration, maximum angular velocity and acceleration, contact force, throwing distance and head contact timing are shown. The differences of landing conditions in secondary impacts of pedestrians and cyclists are also identified. Injury risk curves were generated by logistic regression model for each predicting physical parameters.
Supervision of the safety performance in public transport is one of the main tasks of the Federal Office of Transport (FOT) in Switzerland. Recently a three level system of safety indicators has been defined to cover all means of Swiss public transport. The safety indicators are fed by the FOT incident database since the year 2000. In cooperation with the Institute for Traffic Safety and Automation Engineering (iVA) at TU Braunschweig, Germany, FOT is developing a suitable methodology for the definition and evaluation of the safety targets in Swiss public transport. The methodology is applied for evaluation of safety indicators on a country level and for single transport companies. In a new approach the abovementioned methodology is applied to car incident data to develop an indicator based cross-modal safety measure.
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.
Methods for analyzing the efficiency of primary safety measures based on real life accident data
(2009)
Primary safety measures are designed to help to avoid accidents or, if this is not possible, to stabilize respectively reduce the dynamics of the vehicle to such an extent that the secondary safety measures are able to act as good as possible. The efficiency of a primary safety measure is a criterion for the effectiveness, with which a system of primary safety succeeds in avoiding or mitigation the severity of accidents within its range of operation and in interactionwith driver and vehicle. Based on Daimler-´s philosophy of the "Real Life Safety" the reflection of the real world accidents in the systems range of operation is both starting point as well as benchmark for its optimization. This paper deals with the methodology to perform assessments of statistical representative efficiency of primary safety measures. To be able to carry out an investigation concerning the efficiency of a primary safety measure in a transparent and comparable way basic definitions and systematics were introduced. Based on these definitions different systematic methods for estimating efficiency were discussed and related to each other. The paper is completed by presenting an example for estimating the efficiency of actual "single" and "multi" connected primary safety systems.
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.
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.
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.
Over the last decades the number of traffic accident fatalities on German roads decreased by 77% down to 4968 in the year 2007. This positive development is due to optimisations of vehicle safety, roads and infrastructure and medical rescue issues. Up to now mostly the optimisations of secondary safety measures lead to this effect on vehicle safety. Since some years more and more driver assistance systems are available and lead to a further reduction of all accidents. These new systems are often comfort systems and have not primarily been developed to increase vehicle safety. In contrast to secondary safety systems primary safety systems are able to mitigate and avoid accidents. So in the future it is important to estimate the benefit of these systems in reducing accident numbers as well. Current benefit estimation methods mostly focus on a single system only and not on the combination of systems. In this paper a new method for a multivariate benefit estimation based on real accident data is developed. The paper describes the basic method to estimate the benefit of primary and secondary safety systems in combination. With the presented method the benefit will not be overestimated as it would be by a simple addition of the benefits of single systems. The model will be validated by a multivariate prospective benefit estimation of different vehicle safety systems in comparison to single benefit estimations of the same systems. For this the German In-Depth Accident Database is used. The results show the importance to implement the interactions of safety systems in the estimation process and rate the overestimation by a simple addition of the single system benefits. The validation includes primary and secondary safety systems in combination. The validation is done using more than 3500 real accidents which were initiated by cars. This sample out of the GIDAS database is representative for the current accident situation in Germany. The paper shows the necessity of a multivariate estimation of the benefit for existing and future safety systems.