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Introduction: The method of causation analysis applied under the German accident survey GIDAS, which is based on Accident Causation Analysis System (ACAS) focuses on an on-scene data collection of predominantly directly event-related causation factors which were crucial in the accident emergence as situational resulting events and influences. The paradigm underlying this method refers to the findings of the psychological traffic accident research that most causally relevant features of the system components human, infrastructure and vehicle technology are found directly in the situation shortly before the accident. This justifies the survey method which is conducted directly at the accident (on-scene), shortly after the accident occurrence (in-time) with the detection of human-related causes (in-depth). Human aspects of the situation analysis that interact and influence the risk situations shortly before the collision are reported as errors, lapses, mistakes and failures in ACAS in specific categories and subcategories. Thus methodically ACAS is designed primarily for the collection of accident features on the level of operational action, which certainly leads to valid findings and behavioral causes of accidents. The enhancement by means of Moderating Conditions concerns the pre-crash phase in different levels: strategical, tactical and operational.
The effect of fatigue on driving has been compared to the effect of alcohol impairment in both driver performance and crash studies. However are crash characteristics and causation mechanisms similar in crashes involving fatigue to those involving alcohol when studied in the real world? This has been explored by examining data held in the EC project SafetyNet Accident Causation Database. Causation data was recorded using the SafetyNet Accident Causation System (SNACS). The focus was on Cars/MPV crashes and drivers assigned the SNACS code Alcohol or Fatigue. The Alcohol group included 44 drivers and the Fatigue group included 47. "Incorrect direction" was a frequently occurring critical event in both the Alcohol and Fatigue groups. The Alcohol group had more contributory factors related to decision making and the Fatigue group had more contributory factors relating to incorrect observations. This analysis does not allow for generalised statements about the significance of the similarities and differences between crashes involving alcohol and fatigue, however the observed differences do suggest that attempts to quantify the effect of fatigue by using levels of alcohol impairment as a benchmark should be done with care.
With an ever rising human life expectancy the share of elderly people in society is constantly rising. This leads to the fact that at the same rate the share of people with age related diseases such as dementia and poor eyesight taking part in traffic will rise and therefore traffic accidents caused by this group of people due to the disease will play an ever greater role. This Situation will be among the future challenges of road safety work. At present this study displays specific characteristics of accidents caused by elderly car drivers (aged 65 or higher) based on the analysis of the German In-Depth Accident Study GIDAS. Herein almost 1000 elderly car drivers were identified as accident participants in the years 2008 to 2011. The focus of this study lies on identifying special types of accidents which are caused by elderly drivers and on characterizing these types with the information gathered on scene and by interviewing the participants. The main evidence analyzed is the knowledge about the accident locality, the trajectories of the participants as well as the reasons for the occurrence of the accidents. Furthermore personal information such as the personal condition before the accident and driving purposes is used to identify patterns of contributing circumstances for accidents caused by elderly traffic participants.
From literature well-known analyzes on risks, hazards and causes of accidents of older drivers are amended by the present study in which a comparison of the specific features of accident causes of older car drivers (older than 60 years) and of younger car drivers (under 25 years) is conducted. Mainly the question is pursued if specific errors, mistakes and lapses are predominant in the two different age groups. The analysis system ACAS (Accident Causation Analysis System) used hereby consists of a sequential system of accident causation factors from the human, the technical and the infrastructural field, whereupon for this study the influence of the human features on the accident development in two different age groups is of interest. ACAS is both an accident model and an analysis and classification system, which describes the human participation factors of an accident and their causes in the temporal sequence (from the perceptibility to concrete action errors) taking into consideration the logical sequence of individual basic functions. In five steps (categories) of a logical and temporal sequence the hierarchical system makes human functions and processes as determinants of accident causes identifiable. The methodology specifically focuses on the use in so-called "In-Depth" and "On-Scene" investigation studies. With the help of the system for each accident participant one or more of five hypotheses of human cause factors are formed and then specified by appropriate verification criteria. These hypotheses in turn are further specified by indicators in such manner that the coding of the causation factors by a code system meets the needs of database processing and are accessible to a quantitative data analysis. The first results of the descriptive comparison of the two age groups concern mainly differences in the functional levels "information admission/perception" (where the elderly drivers have more difficulties than the young ones) and "information processing/evaluation" (where the younger drivers show more problems). Concerning the cognitive function of "planning" the group of younger drivers seems to be more often involved in an accident because of excessive speed.
An analysis of NASS and FARS was conducted to determine crash conditions that involved injuries that are not currently being directly addressed by vehicle safety standards or by consumer information test protocols. Analysis of both field data and US NCAP tests were conducted to determine the relative safety provided by seating position and by vehicle model year. Opportunities for improvements were determined by crash categories with large populations of injuries that were not addressed by safety tests or smaller numbers that were increasing in frequency. Areas of opportunities include improved occupant restrain in rollovers, improved frontal protection for rear seat occupants and improved fire prevention in frontal and rollover crashes.
Since 2005, the motorcycle crash fatalities in the US exceeded 10% of the overall annual traffic fatalities. Consequently, it has become critical to gain in-depth understanding of the factors and characteristics contributing to motorcycle crashes. Unfortunately, there currently exists no database gathering the necessary information for an in-depth analysis of the US motorcycle crashes. So this study utilizes the NASS/CDS database (National Automotive Sampling System, Crashworthiness Data System) in order to gain insights into the patterns and factors leading to a NASS/CDS motorcycle crash, from 1997 to 2007. NASS/CDS samples about 5,000 passenger car tow-away crashes per year. Each case includes photographs and detailed data on crash and pre-crash characteristics, vehicle types, trajectories, types of impact, and other pertinent roadway and crash scene information, allowing an in-depth investigation of the crash mechanisms. However, the NASS/CDS sampling process specifically focuses on passenger car crashes, so the cases extracted only correspond to crashes in which a passenger vehicle was towed, and a motorcycle was somehow involved. Thus, a by-hand in-depth review of about 200 cases allowed retrieving 106 relevant crashes for this study, tending to represent the severe passenger vehicle(s) versus motorcycle(s) crashes on US roads. The findings lead to the conclusion that these crashes mostly result from the low conspicuity of the motorcycle, and from the inability of the car drivers to fully appreciate and anticipate the behavior of a motorcycle. Indeed, it has been shown that, first, the car drivers involved in these cases did not attempt any avoidance maneuver, second, they were largely of ages under 25, and finally, the majority of the crashes were in an intersection scenario. In addition, the two major scenarios unveiled were the car attempting a left turn from the opposite direction and the car attempting a left turn from the right. The paper mentions several solutions to enhance the motorcycle- conspicuity and to allow the car drivers to better anticipate its behavior, which seem to be key factors in the intersection-related crashes (and more generally in the passenger vehicle(s) versus motorcycle(s) crashes).
The NHTSA-sponsored Crash Injury Research and Engineering Network (CIREN) has collected and analyzed crash, vehicle damage, and detailed injury data from over 4000 case occupants who were patients admitted to Level-I trauma centers following involvement in motor vehicle crashes. Since 2005, CIREN has used a methodology known as "BioTab" to analyze and document the causes of injuries resulting from passenger vehicle crashes. BioTab was developed to provide a complete evidenced-based method to describe and document injury causation from in-depth crash investigations with confidence levels assigned to the causes of injury based on the available evidence. This paper describes how the BioTab method is being used in CIREN to leverage the data collected from in-depth crash investigations, and particularly the detailed injury data available in CIREN, to develop evidence-based assessments of injury causation. CIREN case examples are provided to demonstrate the ability of the BioTab method to improve real-world crash/injury data assessment.
Unfortunately, there has been a high number of accident fatalities reported in the Czech Republic in recent years. There are many causes which have led to a growth in the number of road traffic accidents. Since 1990, traffic density has demonstrated an upward moving tendency, daily traffic-jams are on the increase in many cities and traffic capacity on roads and streets is not able to satisfy this increasing density. Moreover, many road users lack experience in terms of driving modern cars. The National Accident Study of the Czech Republic is based on the assumption that the year 2010 is considered as a pilot project with the testing operation of collecting and evaluating data from traffic accidents. From the beginning of 2011, a fully-functional structure of the Traffic Accident Research will be created and solid data generated. Based on this assumption, we hope to begin meaningful cooperation with foreign countries.
In India, heavy truck crashes on national highways account for a number of fatalities. But due to lack of in-depth crash data, detailed analysis is not possible to determine injury mechanisms, and to identify infrastructure, vehicle and human factors affecting these crashes. Over the past two years, researchers in India have established a crash investigation network, with the co-operation of the police and hospitals, to conduct crash investigations and in-depth crash data collection on national highways in the state of Tamil Nadu. This pioneering effort has resulted in the development of a heavy truck crash investigation methodology, the outcome of which is scientific and reliable crash data that has been able to provide good insight into truck crashes and their causes. This paper explains the need for truck crash investigations, the methodology, conclusions of the data analyzed up to date, and the need to focus on truck driver working conditions.
Causation patterns and data collection blind spots for fatal intersection accidents in Norway
(2010)
Norwegian fatal intersection accidents from the years 2005-2007 were analysed to identify any causation patterns among their underlying contributing factors, and also to evaluate whether the data collection and documentation procedures used by the Norwegian in-depth investigation teams produces the information necessary to perform causation pattern analysis. A total of 28 fatal accidents were analysed. Details on crash contributing factors for each driver in each crash were first coded using the Driving Reliability and Error Analysis Method (DREAM), and then aggregated based on whether the driver was going straight or turning. Analysis results indicate that turning drivers to a large extent are faced with perception difficulties and unexpected behaviour from the primary conflict vehicle, while at the same time trying to negotiate a demanding traffic situation. Drivers going straight on the other hand have less perception difficulties. Instead, their main problem is that they largely expect turning drivers to yield. When this assumption is violated, they are either slow to react or do not react at all. Contributing factors often pointed to in literature, e.g. high speed, drugs and/or alcohol and inadequate driver training, played a role in 12 of 28 accidents. While this confirms their prevalence, it also indicates that most drivers end up in these situations due to combinations of less auspicious contributing factors. In terms of data collection and documentation, information on blunt end factors (those more distant in time/space, yet important for the development of events) was more limited than information on sharp end factors (those close in time/space to the crash). A possible explanation is that analysts may view some blunt end factors as event circumstances rather than contributing factors in themselves, and therefore do not report them. There was also an asymmetry in terms of reported obstructions to view due to signposts and vegetation. While frequently reported as contributing for turning drivers, they were rarely reported as contributing for their counterparts in the same accidents. This probably reflects an involuntary focus of the analyst on identifying contributing factors for the driver legally held liable, while less attention is paid to the driver judged not at fault. Since who to blame often is irrelevant from a countermeasure development point of view, this underlying investigator mindset needs addressing to avoid future bias in crash investigation reports.