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The German highway network hast o face new challenges in the near future, e.g. increasing traffic density and loads, climate change effects and new quality requirements regarding sustainability. It is necessary to come up with foresighted concepts in the present to be prepared for these challenges. Therefore it is important to adapt and enhance innovative attempts, which take changing impacts into account. One goal of these efforts is the development of adaptive systems for the provision of information and a holistic evaluation in real time. The paper describes the recent research and developments on a system for information and holistic evaluation in real time, taking into account sensor networks, evaluation procedures and their implementation in existing maintenance and inspection strategies.
The German highway network is facing new challenges in the near future. The structures have to deal with increasing traffic loads, climate change effects and new requirements regarding sustainability while they are getting older and budget cuts can be expected. To guarantee a reliable highway network, it will be vital to adapt and enhance innovative approaches. Current bridge management relies on the results of conventional bridge inspections and thus has certain limitations when it comes to insufficient load bearing capacity and other systematic weaknesses. Therefore, new approaches for real time condition assessment of critical road infrastructure elements are to be developed.
Ein auf Nachhaltigkeit angelegtes Großprojekt wie das der Zustandserfassung und -bewertung (ZEB) bedarf eines Qualitätssicherungssystems. Hierfür wurde auf Initiative des Bundesministeriums für Verkehr, Bau- und Wohnungswesen (BMVBW) eine Untersuchung ausgelöst, über die die Grundlagen und Voraussetzungen für ein umfassendes QS-System erarbeitet werden sollen. In einer Pilotierungsphase soll im Jahr 2002 die Machbarkeit nachgewiesen werden.
Enhanced protection of pedestrians and cyclists remains on the focus. Besides infrastructural and behavioral aspects it is necessary to exploit technical solutions placed on motorized vehicles. Accident research needs reliable data as well as national road accident statistics. Changing the view on seriously injured road users is one of the challenges which will substantially contribute to the optimization on future traffic safety. The missing accuracy in the definition of personal injury has a detrimental effect on making cost efficient road safety policy which is not only focused on fatal accidents. The European commission requested that, starting in 2015, all EU member states provide more detailed data on the injury status of road casualties, with special regard to the group of seriously injured. Conventional accident data will always be essential. But to obtain detailed data about driver behavior in real traffic situations further data sources are required. These could be EDR data, data from electronic control units, data from traffic surveys and traffic counting, naturalistic diving studies and field operational tests. Gaining insight into normal as well as critical driver behavior will enable accident researchers to deduct functions estimating the increase or decrease of accident risk associated with certain behaviors or vehicle functions. Also with view to the introduction of highly automated driving functions in the future such data is urgently needed. Computer simulation based tools to estimate the benefits of active safety systems are another step on the way towards the safety assessment of automated driving. It is now the duty of the scientific community to ask the right questions, to develop a methodology and to merge all these data sources into a common framework for the assessment of future traffic safety innovations.
Although the annual traffic accident statistics published by the national police is available in public, the detailed traffic accident data has not been released in Korea. Recently the Ministry of Land, Infrastructure and Transport recognized the importance of in-depth accident data to enhance road traffic safety and initiated a research project to establish a collection of the detailed accident data. The main objective of the project is a feasibility study to establish KIDAS (Korea In-Depth Accident Study). Within this project, three university hospitals which are located in mid-size cities have been selected to collect accident data. Annually, more than 500 cases of accidents have been collected from the in-patient's interviews and diagnosis. Unlike GIDAS (German In-Depth Accident Study), currently on-site investigation can"t be performed by the Korean police. The only available data is patient medical records, patient's description of accident circumstances and the damaged vehicle. Occasionally the police provide the accident investigation reports containing very brief information on accident causation and vehicle safety. In a first step, the concept of KIDAS is to adopt the format of iGLAD (Initiative for the Global Harmonization of Accident Data) for harmonization. Since the currently collected accident information is extremely limited compared with GIDAS, the other sources of data and calculations such as KNCAP vehicle data, pc-crash simulations, vehicle registration information, insurance company data are utilized to complete the iGLAD template. Results from KIDAS_iGLAD and the cases of assessment of active safety devices such as AEBS, ESC, and LDWS will be evaluated.
The changed focus in vehicle safety technology from secondary to primary safety systems need to evolve new methods to investigate accidents, high critical, critical and normal driving situations. Current Naturalistic Driving Studies mostly use vehicles that are highly equipped with additional measuring devices, video cameras, recording technology, and sensors. These equipped fleets are very expensive regarding the setup and administration of the study. Due to the great rarity of crashes it is additionally necessary to have a high distribution and a homogeneous distribution of subject groups. At the end all these facts are leading to a very expensive study with a manageable number of data. Smartphones are becoming more and more popular not only for younger people. Contrary to traditional mobile phones they are mostly equipped with sensors for acceleration and yaw rates, GPS modules as well as cameras in high definition resolution. Additionally they have high-performance processors that enable the execution of CPU-intensive tools directly on the phone. The wide distribution of these smartphones enables researchers to get high numbers of users for such studies. The paper shows and demonstrates a software app for smartphones that is able to record different driving situations up to crashes. Therefore all relevant parameter from the sensors, camera and GPS device are saved for a given duration if the event was triggered. The complete configuration is independently adjustable to the relevant driver and all events were sent automatically to the research institute for a further process. Direct after the event, interviews with the driver can be done and important data regarding the event itself are documented. The presentation shows the methodology and gives a demonstration of the working progress as well as first results and examples of the current study. In the discussion the advantages of this method will be discussed and compared with the disadvantages. The paper shows an alternative method to investigate real accident and incident data. This method is thereby highly cost efficient and comparable with existing methods for benefit estimation.
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.
Um ein zuverlässiges Straßennetz aufrechtzuerhalten, ist es notwendig, neue innovative Ansätze in das Erhaltungsmanagement der Brückenbauwerke im Bundesfernstraßennetz zu integrieren und weiterzuentwickeln. Ergänzend zu den turnusmäßigen Bauwerksprüfungen nach DIN 1076 wird daher ein adaptives Konzept bereitgestellt, das es ermöglichen soll zum einen Zustandsveränderungen frühzeitig zu erfassen und zu bewerten und zum anderen mit Hilfe von erfassten Einwirkungen und Widerständen zukünftige Zustandsentwicklungen zu prognostizieren. Die zu konzipierenden Systeme setzen sich im Wesentlichen aus der Datenerfassung mit Hilfe von Sensorik und den zur echtzeitnahen Verwendung und Bewertung notwendigen Modellen zusammen. Im Rahmen mehrerer Forschungsprojekte wurden einzelne Bausteine eines solchen adaptiven Systems erarbeitet.
The role of a national motor vehicle crash causation study-style data set in rollover data analysis
(2010)
On 1 January 2005, The National Highway Traffic Safety Administration, an agency of the United States Department of Transportation, implemented a new data collection strategy designed to assess crash avoidance technologies and report associated behavioral inputs and outcomes. The original goal was a six-year program, however, during the shortened data collection period; it proved a valuable resource for understanding a precrash environment previously obscured by forensic case investigation. Another unintended consequence was an overlap with infrastructure, roadway geometry, and design with the occupant and vehicle outcomes, by virtue of well-defined attributes. External to the collected data, supplementary information was extrapolated, by using manuals published in the United States, by the American Association of State Highway Transportation Officials and selected State Departments of Transportation, in conjunction with the National Motor Vehicle Crash Causation Study (NMVCCS). This provided a backdrop to the infrastructure framework of the rollover problem within which the occupant and vehicle outcomes were studied. If a NMVCCS-style data collection were to be implemented elsewhere, then complementary manuals produced by federal transportation officials might be consulted producing similar relationships. The current study uses NMVCCS data to describe vehicles travelling through diverse design geometries and the outcome for occupants involved in crashes within that system. Codified and extrapolated data form the basis for assessing NMVCCS and its value to the transportation safety community, as the protocols are applicable universally. The benefit in continuing a NMVCCS-style study is noted, as the interaction of roadway infrastructure and occupant protection agencies might find paths to better work together in solving the complex rollover problem using a common data-driven approach.
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.
Qualität von on-trip Verkehrsinformationen im Straßenverkehr : BASt-Kolloquium 23. und 24.03.2011
(2011)
Am 23. und 24. März 2011 veranstaltete die Bundesanstalt fuer Straßenwesen ein, um die Ergebnisse der erwähnten Projekte und Initiativen präsentieren zu lassen und mit anderen Experten zu diskutieren. Der vorliegende Tagungsband fasst die Ergebnisse des Kolloquiums zur "Qualität von on-trip Verkehrsinformationen" zusammen. Die Bereitstellung von Verkehrsinformationen ist geprägt von vielen Akteuren. Die Wertschöpfungskette beginnt bei der Sammlung grundlegender Verkehrsdaten zur Erstellung von Verkehrsinformationen und setzt sich mit der Datenverarbeitung und -interpretation bis hin zur Meldungserstellung fort. Die Weitergabe kann über verschiedene Übertragungsmedien erfolgen und beim Nutzer (z.B. im Navigationsgerät) empfangen werden. Jeder einzelne Schritt der Wertschoepfungskette kann sowohl von unterschiedlichen Partnern (privat oder öffentlich) übernommen werden als auch in der Hand eines Partners liegen. Diese Komplexität der Zusammenarbeit spiegelt sich demzufolge auch in Qualitätsmanagementprozessen wider. Im Rahmen des Kolloquiums wurden zwei wesentliche Qualitätsaspekte näher betrachtet: - die Datenqualität mit dem Focus auf Aktualitaet, Stimmigkeit der Daten verglichen mit einer gemessenen Realitaet sowohl zu Beginn der Wertschöpfungskette als auch an jeglichen Schnittstellen, - die Prozessqualität, welche sich insbesondere mit der reibungslosen Datenübergabe an den Schnittstellen der Wertschoepfungskette beschäftigt. Beide Qualitätsaspekte helfen zu verstehen, worin die heutigen Qualitätsprobleme bestehen und welche Massnahmen im Einzelnen ergriffen werden müssten, um eine nachhaltige Verbesserung zu erreichen. Einerseits kann es vorkommen, dass die Information über ein Verkehrsereignis an einer oder mehreren Stellen der Wertschöpfungskette korrekt vorliegt, jedoch durch ungenuegende technische oder organisatorische Schnittstellen im Prozessablauf wieder verloren geht und dem Nutzer folglich nicht zur Verfügung steht. Prominentes Beispiel eines solchen Problems in der Prozessqualität ist die fehlerhafte Interpretation der Meldung im Navigationsgerät, denkbar sind solche Informationsverluste jedoch an jeder Stelle der Wertschoepfungskette. Eine wichtige Massnahme zur Verbesserung der Prozessqualität ist die Standardisierung sowie die Überprüfung, ob die definierten Standards an jeder Stelle der Wertschöpfungskette eingehalten werden. Andererseits kann es vorkommen, dass die Datenqualität in Bezug auf ihre Genauigkeit von Anfang an so schlecht ist, dass der Nutzer eine fehlerhafte oder gar keine Nachricht übermittelt bekommt. Beispiel hierfür ist die Vielzahl von Stauereignissen, die entweder nicht gesendet wurden oder gesendet wurden, obwohl sie nicht vorhanden waren. Eine wichtige Massnahme zur Verbesserung dieser Situation ist die Verbesserung der Ereignisdetektion. Der Tagungsband enthaelt Präsentationen, die den Status Quo analysieren, Methoden zur verbesserten Datenerfassung vorschlagen und Möglichkeiten zur Verbesserung von Daten- und Prozessqualität vorstellen. Offen geblieben sind darüber hinaus folgende Fragestellungen: - Wie kann die Prozessqualität der gesamten Wertschöpfungskette bei der Vielzahl der Partner kontrolliert werden? Wer überwacht die Wertschöpfungskette? Wird hierfuer überhaupt eine zentrale Stelle benötigt? Oder ist es ausreichend, wenn jeder Partner eine angemessene Eingangs bzw. Ausgangskontrolle durchführt? - Obwohl es nur eine Realität gibt, entsteht doch Wettbewerb über die (Qualität der) Information zu dieser Realität. Wie kann Konsistenz zwischen allen Anbietern von sicherheitsrelevanten Informationen erreicht werden? Wo sollte der Wettbewerb enden und wie kann dies technisch, organisatorisch und wirtschaftlich realisiert werden? - Welche Prozesse sollten geschaffen werden, um Partner zu integrieren, die sich nicht an geschaffene Qualitätsstandards halten (z.B. kommerzielle Diensteanbieter, die nicht mit der Verkehrsinformationsszene vernetzt sind)? - Und nicht zuletzt, wie kann die Wahrnehmung des Nutzers über verschiedene Qualitätslevel unterschiedlicher Produkte verbessert werden? Ist der Nutzer in der Lage, die unterschiedlichen Qualitätsstufen von Verkehrssystemen zu unterscheiden? Falls nicht, welche Art von Unterstuetzung braucht der Kunde? Ein "European Information Services Assessment Programme" vergleichbar zu Euro NCAP für Fahrzeuge? Die Ergebnisse des Kolloquiums sollen die laufende Diskussion um die Verbesserung der Qualität von Verkehrsinformationen unterstützen.
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.
A national initiative from the vehicle manufacturers, safety system suppliers, the road administration and universities in Sweden took off in 2007. The aim was to develop a national investigation network and a methodology focusing on all phases of a crash (pre-crash, in-crash and post-crash) as well as all parts of the road transport system (road user, vehicle and road environment). The initiative is formally run as a project with the acronym INTACT (Investigation Network and Accident Collection Techniques). It was a three year pilot with the aim to develop methodologies for an extended national crash investigation activity. During the first year the INTACT partners agreed on the aim for the investigation and methods for retrieving the data were developed. During the second and third year the methodology was tested in real-world investigations and further refinement was made. The paper describes the methodology developed to obtain high qualitative in-depth road crash data.
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.
Accidents with vulnerable road users require special attention within the road safety work because these accidents are often accompanied with severe injuries. Thus In 2006 at least 6200 Powered Two Wheeler (PTW) riders were killed in road crashes in the EU 25 representing 16% of the total number of road deaths while accounting for only 2% of the total kilometers driven. For the prevention of accidents with VRU above all the knowledge of the causes of the accidents is of special importance. This study is based on the methodology of the German In-Depth Accident Study GIDAS. Within GIDAS extensive data on various fields of accidentology are collected on-scene from road traffic accidents with injuries in the Hannover and Dresden area. Using a well defined sample plan the collected data is highly representative to the whole German situation (Brühning et al, Otte et al). The need of in-depth accident causation data in accident research led to the development of a special tool for the collection of such data called ACASS (Accident Causation Analysis with Seven Steps), which was implemented in the GIDAS methodology in 2008 and described by Otte in 2009.
Adverse weather could impair the performance of many important parts in road transportation. In a tropical country, the threats posed by the weather phenomenon can be viewed from a different perspective as the situation may not be as extreme as snow-related problems or excessive temperature in other countries. Specifically in Malaysia, the situation may be underestimated due to several reasons such as the deficiencies in accident reporting and lack of research work. This background research has looked into various publications as well as related data to explain the need of more comprehensive research in the future.
Impact severity is a fundamental measure for all in-depth crash investigation projects. One methodology used in the UK is based on the US Calspan software package CRASH3. The UK- in-depth crash investigation studies routinely use AiDamage3 a software package which is based on an updated version of the original CRASH3 algorithm, including enhancements to the vehicle stiffness coefficients. Real world accident-damaged vehicles are measured and their crush is correlated with a library of stiffness coefficients. These measurements are then used, along with other parameters, to calculate the crash energy and equivalent changes of velocity of the vehicles (delta-v), which is a measure of the impact severity. UK in-depth accident studies routinely validate the crash severity methodologies applied as the vehicle fleet changes. This is achieved by analysing crash test data and using the appropriate residual crush damage and other inputs to AiDamage3 and checking the program- outputs with the known crash severity parameters. This procedure checks, at least in part, the default stiffness values in the data libraries and the reconstruction methods used.
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.