Accident data sources

The RMC HARIS (Hazards and Reliability Information System) programs provide organizations with a data ban)c of reliability, maintainability, accident, and source-abstract data. The programs permit the input of information in a standard data sheet format. Search capability is built into the programs for retrieval of these data sheets against specific search profiles. HARIS presently contains over 4400 data sheets. 

Comparison of different sources of data (specific accident data for a mode as compared to generic data)... 

Generic Data Data that are built using inputs from all operations within a company, from literature sources, from past TRA reports, and from commercial databases such as accident data for all vehicles vs. tracks only vs. certain types of trucks. 

Most of the data presented and analysed in this chapter has been collected from the ten participating countries during the period of our project within the ADB-ASEAN-ASNet program and visits to the region (in 2004). The ASNet database, we improved, is one important source of accident data and information from the region. It includes accident statistics from the region, country reports, external links to other international reports (e g. ADB reports), and much more. But some other data remains to be collected from other international sources such as the World Bank, the United Nations, and the IRF International Road Federation Database. 

As both retrospective and prospective evaluation methods are based in many cases on accident data, a short summary concerning possibilities and limitations of accident data as well as other data sources is given in the following. These general findings have effects on the validity of each method discussed below and are not dependent on the specific method used. 

The individual accidents are grouped into so-caUed reference scenarios . Reference scenarios are defined as a limited number of scientifically derived traffic situations that represent a major part of the real traffic system [2]. Basis for the construction of reference scenarios are in this case in-depth accident data. A detailed description of the data sources, the development of the methodology as well as the results can be found in [2 ]. The grouping uses parameters that have a high influence on the genesis of the critical situation and describe the pre-crash phase in a meaningful way. In the case of pedestrian accidents the key criteria are ... 

The accuracy as well as the power of the models depend on the number of cases available resulting practical limitations to research were highlighted using the GHDAS data base as an example. Especially for the construction of injury probability models, data sources should be up to date and should include far more cases. In addition, the accuracy of the models depend on the quality of the data used continuous improvements in coding and reconstruction are thus strongly encouraged. Imputation procedures, as included, for example, in US accident data bases, could minimize loss of data due to list-wise deletion in a standardized way. 

In order to develop and maintain a safety metrics program, various resources must be identified. These resources include data sources and measurement techniques. If a more traditional approach to measuring safety performance is utilized, such as accident and injury rates, the data should be available and if not, part of the metrics program should involve the procedures for collecting the data and collating it at a central location for analysis. 

This article aims to provide a practical view to accident risks and risk assessment in industrial maintenance. The focus is on the accident sources, based on real accident data. In addition, the article provides information regarding risk assessment and management in practice. 

The sorts of information listed above are usefui only if they are stored in a systematic manner and there are suitable mechanisms for the retrieval of the relevant data. Similar considerations apply when the data come, not from an outside source, but from within the organisation. For example, accident data (both injury and damage), sick absence and product complaints are all internally generated data. It is necessary to set up systematic methods of collecting, storing and retrieving these data if the best use is to be made of them. 

Accident data are only one form of risk information which assists in the identification, assessment and control of risks. Other forms of risk information include reliability data epidemiological studies mortality/morbidity data vulnerability analysis results of audits/inspections insurance claims data and - probably the best - personal experience. Sources of risk information are listed in the Further Reading section of this chapter (see especially the Handbook of Risk Management by Carter et ah). 

Figure 8.1 Potential data sources for accidents and incidents in general aviation (for operations covered in the BTRE definition of GA)...

Because system safety is really a field that crosses all parts of engineering and society, you should not limit your thinking to the traditional data sources. Obviously, the first place to start is with your company, looking at the historical data on similar systems, past accidents (and near misses— we can learn a lot from what we barely averted), trend analyses, engineering reports, and analyses. 

The accident data was gathered from a book published by the operator (NISOC, 2011). This may not the complete source of data, since usually incident histories are not published publicly as a result of confidentiality issues and it is evident that this document focused on major events, those that caused loss of life, damage to property or harm to the environment. It is, nevertheless, useful for our purpose, which is the identification of the main accident scenarios in this sector because such major incidents can be treated as representative of the major scenarios that are likely. If some minor scenarios are missed they will appear to be either associated with lesser consequences or be much less frequent. We then compared incidents within this specific area with incidents of the same industry in world-wide over the same period (Sam 2005). 

Ladan, M. Hanninen, M. 2012 Data Sources For Quantitative Marine Traffic Accident Modeling. Aalto University Publication Series Science + Technology 1112012. Espoo, Aalto University. 

The marine accident statistics produced in this paper, are based on the data from winters December 2002 to April 2003 and October 2009 to April 2013. The data sources used for this analysis are provided by the Finnish Transport Safety Agency (Trafi), which consist of marine casualties reported on Finnish waters or to vessels registered to Finland. In the particular case of the reports from winters 2009-2013, identification of the accident locations is based on the coordinates of the accidents reported in that time period. And for the purposes of this paper, those coordinates have been allocated within the ice maps reported by SMHI and FMI on those dates. More details such as the kind of vessel involved in the accidents. 

In addition, the data collection and interpretation process could lead to significantly different results, depending on the data source and the acquisition method (field data, simulator data, HRA modelling, expert ehcitation, operation observation, reporting methods, near misses, performance indicators, accident investigation reports etc.). 

For this study, we analyzed all records of injirry acciderrts from the natiortal Austrian accident database, where cyclists were involved. The data covers the years 2002 until 2011 since data for 2012 was not available at the time of writing. While studies [ELV 99, LAN 03] recommend the use of hospital data for safety assessments, police data for accidents was used in this study since hospital data is not available for analysis in Austria. While accident insurance providers do collect data from hospitals, current data privacy laws do not allow for a consolidation of accident data from police and hospital sources. For the city of Vienna, the resulting dataset contains a total of 6,287 accidents. The database consists of several tables which describe the accidents. The accident table contains information about the location of the accident, the weather conditions, as well as the date and type of the accident. The participants table contains information about the participants such as age, degree of injury and type of vehicle. Table 10.4 shows the trends of the yearly nnmber of accidents. Fignre 10.1 presents a comparison of the trends of accident counts and bicycle counts based on the initial values for 2002. In the analysis time frame between 2002 and 2011, the data show no correlation between trends of accident counts and bicycle counts (R = 0.03). This suggests the validity of the concept of safety in numbers , which states, that an increase in the modal share of bicycles leads to a decrease in the number of accidents per cycled kilometer. 

Offshore Oil and Gas Industry Accident Databases and Accident Data Collection Sources... 

Safety Evaluation Areas (SEAs) involved SafeStat analytically assessing a motor carrier in four Safety Evaluation Areas (SEAs) Accident SEA, Driver SEA, Vehicle SEA, and Safety Management SEA. Each SEA was based on two or more indicators supported by different data sources. The SEAs were replaced by the seven Behavior Analysis Safety Improvement Categories (BASICs) under the Compliance, Safety, Accountability (CSA) model that uses the Safety Measurement System (SMS) instead of SafeStat. 

Part V is a descriptive section. It presents different methods of risk analysis. The selected methods fall within the scope of this book, since the employee s experience of incidents and accidents is a basic data source in the analysis. 

Chapters 7 and 8 are devoted to rail safety and to truck and bus safety, respectively. Chapter 7 covers topics such as causes of railway-related accidents and incidents, general classifications of rail accidents by effects and causes, rail derailment accidents and incidents and their causes, telescoping-related railway accidents, railway accidents in selected countries, railroad tank car safety, and methods for performing rail safety analysis. Some of the topics covered in Chapter 8 are top truck and bus safety issues, truck safety-related facts and figures, the most-cited truck safety-related problems, safety-related truck inspection tips, bus and coach occupant fatalities and serious injuries, transit bus safety and key design-related safety feature areas, and vehicle safety data sources. 

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