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Accident Models for Modern Road Traffic: Changing Times Creates New Demands
Linköpings universitet.
Volvo Cars Safety Centre.
Chalmers University of Technology.ORCID iD: 0000-0002-4169-610X
Linköpings universitet.
2004 (English)In: Proceedings of the International Conference on Systems, Man and Cybernetics, 2004Conference paper, Published paper (Other academic)
Abstract [en]

The purpose of this study is to develop accident models that can be applied to modern road traffic. Several criteria are proposed that a model suitable for the conditions of modern road traffic should fulfil. Four commonly applied general accident models are reviewed, and found to be inadequate in relation to the criteria. Also, the consequences of an underlying structural problem in all four model types, which is the result of regarding the human as a system component, are discussed. To remedy the discovered problems, it is argued that traffic safety should make use of the developments that have been made in the field of industrial safety. Several suggestions are proposed for how a new model could be developed, based on experiences from industrial safety.

Place, publisher, year, edition, pages
2004.
Keywords [en]
Accident, Cause, Classification, Driver, Behaviour
National Category
Engineering and Technology
Research subject
80 Road: Traffic safety and accidents, 81 Road: Accidents; 80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
URN: urn:nbn:se:vti:diva-13356DOI: 10.1109/ICSMC.2004.1398310OAI: oai:DiVA.org:vti-13356DiVA, id: diva2:1259871
Conference
International Conference on Systems, Man and Cybernetics. October 10-13, The Hague, The Netherlands
Available from: 2007-02-26 Created: 2018-10-31 Last updated: 2019-03-13Bibliographically approved
In thesis
1. Aggregating Case Studies of Vehicle Crashes by Means of Causation Charts: An Evaluation and Revision of the Driving Reliability and Error Analysis Method
Open this publication in new window or tab >>Aggregating Case Studies of Vehicle Crashes by Means of Causation Charts: An Evaluation and Revision of the Driving Reliability and Error Analysis Method
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is a need for increased knowledge about causes to motor-vehicle crashes and their prevention. Multidisciplinary in-depth case studies can provide detailed causation data that is otherwise unattainable. Such data might allow the formulation of hypotheses of causes and causal relationships for further study. By converting the data into causation charts that are aggregated, common causation patterns would give greater weight to such hypotheses. However the charts must first be compiled by means of a systematic analysis method, which requires three parts; a model, a classification scheme and a classification method.

Four general accident models were evaluated and found inadequate to form the basis for a causation analysis method. This was primarily because the models in practice treat road-users, vehicles and traffic environment as separate components, but also due to the focus on events immediately prior to the crash and either static, sequential, or absent modelling of interaction.

Two studies were carried out to evaluate whether case files could be aggregated by means of charts that had been compiled with the Driving Reliability and Error Analysis Method (DREAM). In DREAM, contributory factors (genotypes) are systematically analysed, classified and linked in a single chart for each driver that illustrate the causes of a critical event (phenotype). In the first study, case files from 38 single-vehicle crashes were examined to distinguish crashes with similar circumstances. Four types of loss of vehicle control were identified, for which the associated DREAM charts were aggregated. The results revealed common patterns within the types, as well as different patterns between them. The second study focused on 26 intersection crashes. Based on the most common violations at intersections, six risk situations were defined, and the DREAM charts associated with each risk situation were aggregated. A common pattern in each of two risk situations indicated that drivers with and without the right of way had not seen the other vehicle due to distractions and/or sight obstructions. A frequently occurring pattern for the drivers with the right of way was that they had not expected another vehicle to cross their path. The absence of clear patterns in three risk situations was a result of a low number of charts and rather unique circumstances in these cases. Parts of the aggregated charts contained an unexpectedly large variation, identified as a consequence of inconsistently compiled charts.

Prior the final study assessing intercoder agreement, DREAM was revised into a new version based on the experience from the latter aggregation study. A total of seven investigators from four European countries compiled seven DREAM charts for each driver involved in four types of accidents. The results indicated that the intercoder agreement for genotypes ranged from 74% to 94% with an average of 83%, while it for phenotypes ranged from 57% to 100% with an average of 78%. This acceptable level of agreement is expected to rise with enhanced training. The present thesis thus shows that DREAM is a highly promising method for the compilation of causation charts. Future studies are expected to benefit from aggregating DREAM charts when formulating hypotheses of general causes and causal relationships as a subject for further research, as well as to identify alternative countermeasure strategies.

Place, publisher, year, edition, pages
Göteborg: Chalmers University of Technology, 2008
Series
Doktorsavhandlingar vid Chalmers tekniska högskola, Ny serie, ISSN 0346-718X ; 2849
Keywords
Accident, Cause, Classification, Characteristics, Driver, Error
National Category
Other Mechanical Engineering
Research subject
80 Road: Traffic safety and accidents, 81 Road: Accidents; 80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
urn:nbn:se:vti:diva-11683 (URN)978-91-7385-168-8 (ISBN)
Supervisors
Available from: 2018-10-31 Created: 2017-03-13 Last updated: 2019-03-13Bibliographically approved

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Huang, Yu-HsingSandin, JesperHollnagel, Erik

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