Sonstige
NASS: the glass is half full
(2007)
The National Accident Sampling System (NASS) was born in the late 1970s. It was based on a substantial amount of experience and analysis of what was needed in the United States to understand the safety challenges of our highways. This work also showed how to collect high quality and useful crash data efficiently. Unfortunately, when Ronald Reagan - a President who believed in limited government - was elected, any hope of full funding for NASS was lost. The concept of 75 teams investigating about 18,000 serious crashes in detail annually was never realized. The system got up to 50 teams, then was cut to 36, and finally to 24 teams investigating fewer than a quarter of the originally anticipated number of crashes per year. Despite this, the NASS investigations provide a rich source of data, collected according to a sophisticated statistical sampling system to facilitate detailed national estimates of road casualties on our nation- highways and their causes. In addition, changes have been made in recent years to increase the number of more serious crashes of recent model vehicles to make the results more relevant to improving vehicle safety. A recent, detailed examination of hundreds of rollovers has provided considerable insight into rollover casualties and into what can be done to reduce them. Some of these results will be presented that show the value of the NASS system. Our experience with NASS and the Fatal Accident Reporting System (FARS) suggests a number of improvements that could be made in the United States" crash data systems. It also provides justification for a doubling or tripling of our national expenditures on crash data collection.
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 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.
This paper reviews briefly the evolution of the investigation of transport accidents from the early beginnings when individual events were studied but systematic data was not collected. In the transport modes other than on the roads, accident investigation early on, even of single events, was important in introducing safety improvements. Road accidents, however, evolved enormously with the growth of car ownership without any comparable political response to the consequent deaths and injuries, equivalent to what happened with the other modes. From the 1950s data bases started to contribute to our knowledge of the epidemiology of road traffic injuries, and in-depth sample studies have contributed much to the body of knowledge in the last 30 years. However, even the basic input and output variables of a crash, its severity and the seriousness of the outcomes in terms of injuries and their consequences are not complete or agreed upon. Issues of experimental design and sampling are discussed. It is proposed that the most important area for current research to address is the effect of population variations on injury outcomes. The need for the establishment of good data bases for active safety issues is emphasised with the consequent need for better links between the research community and the police.
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.
In the context of this study, different data sources for accident research were examined regarding their possible data access and evaluated concerning the individual quality and extent of the data. Analyses of accidents require detailed and comprehensive information in particular concerning vehicle damages, injury patterns and descriptions of the accident sequence. The police documentation supplies the basic accident statistics and is amended in the context of the forensic treatment by further information, e.g. by medical and technical appraisals and witness questionings. As a new approach to the data acquisition for the analysis of fatal traffic accidents, the information was made usable which was collected by the police and by the investigations of the public prosecutor. The best strategy for obtaining reliable, extensive and complete data consists of combining the information from these two sources: the very complete, but elementary statistic data of the Niedersächsisches Landesamt für Statistik (Lower Saxony State Authority of Statistics), based on the police documentation as well as the very extensive accident information resulting from the investigation documentation of the public prosecutor after conclusion of the procedure, the so-called Court Records. Of all 715 fatal traffic accidents, which happened in the year 2003 in the German State of Lower Saxony, 238 cases were selected by means of a statistically coincidental selective procedure based on a statistically representative manner (every third accident). These cases cover the investigation documents of the 11 responsible public prosecutor- offices, which were requested and evaluated while preserving the data security. Of the 238 cases 202 cases were available, which were individually coded and stored in a data base using 160 variables. Thus a data base of a sample of representative data for fatal accidents in Lower Saxony was set up. The data base contains extensive information concerning general accident data (35 variables), concerning road and road surface data (30 variables), concerning vehicle-specific data (68 variables) as well as concerning personal and injury data (27 variables).
Data concerning accidents involving personal injury which have been collected in the context of in-depth investigations on scene in the Hannover area since 1973 and in the Dresden area since 1999 represent an important basis for empirical traffic safety research. At national and international level various analyses and comparisons are carried out on the basis of "in-depth data" from the above mentioned investigations. In-depth data play a decisive role e.g. within the validation of EuroNCAP results on secondary safety (crashworthiness) of individual passenger car models. Thus, statistically sound methods of data analysis and population parameter estimation are of high importance. Since the 1st of August 1984 the "in-depth investigations on scene" in the Hannover area have been carried out according to a sampling plan developed by HAUTZINGER in the context of a research project on behalf of BASt. In the meantime a second region of in-depth investigation on scene was added with surveys in Dresden and the surrounding area. Internationally, the acronym GIDAS (German In-Depth Accident Study) is commonly used for the two above mentioned surveys. The objective of a current research project (topic of this contribution) is, among other things, to examine and adjust the previous weighting and expansion method for the two regional accident investigations to the current general conditions.
The data situation for quantifying the proportion of accidents avoided by the introduction of active safety systems is incomplete, since there is generally no data available on the accidents avoided by the technology in question. In this paper, a split-register approach is suggested and compared with the classical case-control approach known from epidemiologic applications. Provided a set of assumptions hold, which can reasonably be made in such data situations, the split register approach allows inferences on the population accident risk. For both approaches the benefits of basing the analysis on the results of a logistic regression to adjust for confounding factors are outlined. The biasing effects of violating key assumptions are discussed and the split-register approach is demonstrated using the example of the active safety system ESP with data from the German in-depth accident study GIDAS.
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.