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- Title
Bayesian reconstruction of traffic accidents.
- Authors
Davis, Gary A.
- Abstract
Traffic accident reconstruction has been defined as the effort to determine, from whatever evidence is available, how an accident happened. Traffic accident reconstruction can be treated as a problem in uncertain reasoning about a particular event, and developments in modeling uncertain reasoning for artificial intelligence can be applied to this problem. Physical principles can usually be used to develop a structural model of the accident and this model, together with an expert assessment of prior uncertainty regarding the accident's initial conditions, can be represented as a Bayesian network. Posterior probabilities for the accident's initial conditions, given evidence collected at the accident scene, can then be computed by updating the Bayesian network. Using a possible worlds semantics, truth conditions for counterfactual claims about the accident can be defined and used to rigorously implement a ‘but for’ test of whether or not a speed limit violation could be considered a cause of an accident. The logic of this approach is illustrated for a simplified version of a vehicle/pedestrian accident, and then the approach is applied to four actual accidents.
- Subjects
BAYESIAN analysis; TRAFFIC accidents; ACCIDENT statistics; COMMUNICATIONS industries; SEMANTIC networks (Information theory); COMPUTER network monitoring equipment; PEDESTRIAN accident investigation; ARTIFICIAL intelligence; COMPUTER software
- Publication
Law, Probability & Risk, 2003, Vol 2, Issue 2, p69
- ISSN
1470-8396
- Publication type
Article
- DOI
10.1093/lpr/2.2.69