articleTop 10% cited
Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks
Accident Analysis & Prevention · 2005 · Vol. 38(3) · pp. 434–444
Dursun Delen✉(Oklahoma State University)Ramesh Sharda(Oklahoma State University)Max Bessonov(Oklahoma State University)
Traffic and Road SafetyTraffic Prediction and Management TechniquesOccupational Health and Safety ResearchCrashPoison controlInjury preventionHuman factors and ergonomicsArtificial neural networkEngineeringOccupational safety and healthSample (material)Transport engineeringComputer science
MeSH terms
Accidents, TrafficAutomobile DrivingDemographyEnvironment DesignHumansRisk FactorsSafetySeverity of Illness IndexUnited StatesNeural Networks, ComputerNonlinear DynamicsRisk Assessment
Funding
- Ford Motor Company
Citations
424
FWCI
13.62
field-weighted impact
References
31
Percentile
98%
vs. same field & year
Citations per year
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References
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
Neural Networks · 1990 · 2,101 citations
Driver injury severity: an application of ordered probit models
Accident Analysis & Prevention · 2002 · 607 citations
Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections
Transportation Research Record Journal of the Transportation Research Board · 2001 · 300 citations
Using logistic regression to estimate the influence of accident factors on accident severity
Accident Analysis & Prevention · 2002 · 496 citations
Classification and regression trees
European Journal of Operational Research · 1985 · 10,158 citations
Classification and Regression Trees.
Journal of the American Statistical Association · 1986 · 21,013 citations
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