An Information Renewal GNN Model for Road Traffic Accident Forecasting
Publication: International Conference on Transportation Engineering 2009
Abstract
Road traffic system is a complicated non-linear system, in which road traffic accident is considered as the behavioral characteristic variable, whose developmental changes have trends of increase and stronger random fluctuation. Considering this point, we establish a combination forecasting model (namely GNN) based on grey forecasting model and ANN. First, grey information renewal GM (1,1) is established based on GM (1,1) and used to forecast the change tendency, then BP network is applied to modify grey residuals to capture stochastic phenomenon. The results show that the dual character of the road traffic time series with trends of increase and random fluctuation can be better described. Meanwhile, with advantages of GM (1,1) and ANN, GNN has obtained better forecasting precision than single grey information renewal GM (1,1). In summary, GNN can be applied as a novel, practical and simple forecasting tool in road traffic accident forecasting.
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Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Forecasting
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Mathematics
- Models (by type)
- Neural networks
- Probability
- Statistics
- Stochastic processes
- Time series analysis
- Traffic accidents
- Traffic engineering
- Traffic management
- Traffic models
- Transportation engineering
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