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Aggregating Case Studies of Vehicle Crashes by Means of Causation Charts: An Evaluation and Revision of the Driving Reliability and Error Analysis Method

Jesper Sandin (Institutionen för tillämpad mekanik, Fordonssäkerhet)
Göteborg : Chalmers University of Technology, 2008. ISBN: 978-91-7385-168-8.- 128 s.

There is a need for increased knowledge about causes of 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.

Nyckelord: driver error, collision avoidance, pre-crash, fatigue, slipperiness, rollover, young drivers, vehicle dynamics

Denna post skapades 2008-08-29. Senast ändrad 2013-09-25.
CPL Pubid: 73377


Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Fordonssäkerhet (2005-2017)


Matematisk statistik
Övrig teknisk mekanik

Chalmers infrastruktur

Relaterade publikationer

Inkluderade delarbeten:

Accident models for modern road traffic: changing times creates new demands*

Understanding the causation of single-vehicle crashes: a methodology for in-depth on-scene multidisciplinary case studies


Datum: 2008-09-26
Tid: 09:00
Lokal: HA3
Opponent: Associated Professor Dominique Fleury, the French National Institute for Transport and Safety Research (INRETS), Acrueil Cedex

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Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie 2849