ANN Estimate Model on Impact Speed of Car-Bicycle Accidents Based on the Complete Information
Publication: International Conference on Transportation Engineering 2009
Abstract
Through the investigation on car-to-bicycle accidents of Beijing city, the characteristic impact parameters of human, bicycle and vehicle are collected. The impact speeds are classified by statistic results and law regulation. Above that, based on the Artificial Neural Network (ANN) method, relevant typical parameters are selected to build an estimate model on vehicle impact speed of car-to-bicycle accidents. The input layer of the model contains 45 nodes, including human parameters, bicycle parameters, car parameters, road parameters and other information. The output layer of the model represents the forecasting data and different classification results of vehicle impact speed respectively. Using the credible data from real accidents, the model can be trained and applied in vehicle-speed analysis and accident reconstruction.
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Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Accidents
- Artificial intelligence and machine learning
- Bicycles
- Business management
- Computer programming
- Computing in civil engineering
- Data collection
- Engineering fundamentals
- Highway transportation
- Human and behavioral factors
- Infrastructure
- Mathematics
- Methodology (by type)
- Neural networks
- Parameters (statistics)
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Statistics
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Vehicle impacts
- Vehicles
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