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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.
Nowadays, traffic accidents are recorded in historical databases. Regarding the huge quantity of data, the use of data mining tools is essential to help Experts, for automatically extracting relevant information in order to establish and quantify relations between severity and potential factors of accidents. An innovative approach is here proposed for an in depth investigation of real world accidents data base. Mutual information ratio based on conditional entropies is used to quantity the association strength between an accident outcome descriptor (injury severity) and other potential association factors. Information theoretic methods help to select automatically groups of factors mostly responsible of the severity of accident.
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.
A lack of representative European accident data to aid the development of safety policy, regulation and technological advancement is a major obstacle in the European Union. Data are needed to assess the performance of road and vehicle safety and is also needed to support the development of further actions by stakeholders. This short-paper describes the process of developing a data collection and analysis system designed to partly fill these gaps. A project team with members from 7 countries was set up to devise appropriate variable lists to collect fatal crash data under the following topic levels: accident, road environment, vehicle, and road user, using retrospective detailed police reports (n=1,300). The typical level of detail recorded was a minimum of 150 variables for each accident. The project will enable multidisciplinary information on the circumstances of fatal crashes to be interpreted to provide information on a range of causal factors and events surrounding the collisions.
The Powered Two Wheelers (PTWs) accidents constitute one of the road safety targets in Europe. PTWs users' fatalities represent 15% of EU road fatalities, having increased the last few years, which is quite opposite than other road users casualties. To reduce PTW accidents is necessary to know which the accident causations are from different points of view (human factor, vehicle characteristics, environment, type of accident, situation, etc.). In TRACE project ("Traffic Accident Causation in Europe", under the European Commission 6th Framework Program, 2006-2008,) a specific task was focused on PTW users point of view, analyzing extensive databases to locate the main accident configurations (type of accident, severity, frequency), and an in-depth database to obtain the causation factors, the risk factors for each configuration founded in the extensive databases analysis and the variables associated to each causation factor in the PTW configurations.
In an on-going project since 2005, ADAC has been analyzing accidents documented by the ADAC air rescue service. The knowledge derived from real-life accidents serves as a basis for new test configurations and assessment criteria. In 2007, ADAC began looking into the feasibility of international data collection. The idea of Global Accident Prevention was born. Three European partner clubs have begun pioneering the project (ÖAMTC, ANWB, and RACC). The aim is to set up an international accident research network to provide a steady stream of information on road accidents. The FIA Foundation supports ADAC in developing and coordinating this initiative.
In Germany, in-depth accident investigations are carried out in the Hannover area since 1973. In 1999 a second region was added with surveys in Dresden and the surrounding area. Internationally, the acronym GIDAS (German In-Depth Accident Study) is commonly used for these surveys. Compared to many other countries, the sample sizes of the GIDAS surveys are much larger. The goal is to collect 1.000 accidents involving personal injuries per year and region. Data collection takes place by using a sampling procedure, which can be interpreted as a two-stage process with time intervals as primary units and accidents as secondary units. An important question is, to what extend these samples are representative for the target population from which they are drawn. Analyses show, for example, that accidents with persons killed or seriously injured are overrepresented in the samples compared to accidents with slightly injured persons. This means, that these data are subject to biases due to uncontrolled variation of sample inclusion probability. Therefore, appropriate weighting and expansion methods have to be applied in order to adjust or correct for these biases. The contribution describes the statistical and methodological principles underlying the GIDAS surveys with respect to sampling procedure, data collection and expansion. In addition, some suggestions regarding potential improvements of study design are made from a methodological point of view.
Annually within the European Union, there are over 50,000 road accident fatalities and 2 million other casualties, of which the majority are either the occupants of cars or other road users in collision with a car. The European Commission now has competency for vehicle-based injury countermeasures through the Whole Vehicle Type Approval system. As a result, the Commission has recognised that casualty reduction strategies must be based on a full understanding of the real-world need under European conditions and that the effectiveness of vehicle countermeasures must be properly evaluated. The PENDANT study commenced in January 2003 in order to explore the possibility of developing a co-ordinated set of targeted, in-depth crash data resources to support European Union vehicle and road safety policy. Three main work activity areas (Work Packages) commenced to provide these resources. This paper describes some of the outcomes of Work Package 2 (WP2, In-depth Crash Investigations and Data Analysis). In WP2, some 1,100 investigations of crashes involving injured car occupants were conducted in eight EU countries to a common protocol based on that developed in the STAIRS programme. This paper describes the purposes, methodology and results of WP2. It is expected that the results will be used as a co-ordinated system to inform European vehicle safety policy in a systematic, integrated manner. Furthermore, the results of the data analyses will be exploited further to provide new directions to develop injury countermeasures and regulations.
The need for improved EU level accident information and data was identified in the EU White Paper on Transport Policy (2001)1 and detailed in the Road Safety Action Plan (2003)2. The plan specifies that the EC will develop a road safety observatory to coordinate data collection within an integrated framework.
This study is aimed to investigate the correlations of impact conditions and dynamic responses with the injuries and injury severity of child pedestrians by accident reconstruction. For this purpose, the pedestrian accident cases were selected from Sweden and Germany with detailed information about injuries, accident cars, and accident environment. The selected accident cases were reconstructed using mathematical models of pedestrian and passenger car. The pedestrian models were generated based on the height, weight, and age of the pedestrian involved in accidents. The car models were built up based on the corresponding accident car. The impact speeds in simulations were defined based on the reported data. The calculated physical quantities were analyzed to find the correlation with injury outcomes registered in the accident database. The reconstruction approaches are discussed in terms of data collection, estimating vehicle impact speeds, pedestrian moving speeds and initial posture, secondary ground impact, validity of the mathematical models, as well as impact biomechanics.