Traffic Incident Duration Prediction Based on the Bayesian Decision Tree Method
Publication: Transportation and Development Innovative Best Practices 2008
Abstract
Effective incident management requires a full understanding of various characteristics of incidents to accurately estimate incident durations and to help make more efficient decision to reduce the impact of non-recurring congestion due to these accident. This paper presents a new prediction model base on the Bayesian decision model to estimate the traffic incident duration. The basic theory of Bayesian decision model is presented and the prediction model is created based on this theory using incident data collected in Rijkswaterstaat Verkeerscentrum Nederland from various sources. Compared to most existing methods, the proposed model is unique in two aspects: firstly, the model is adaptive in the presence of real incident for which data might only be partially available or in the presence of incomplete information. Secondly, this model is shown to perform better theoretical prediction accuracy compared to the decision model or Bayesian model.
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Copyright
© 2008 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Business management
- Coastal engineering
- Coastal management
- Coasts, oceans, ports, and waterways engineering
- Ecosystems
- Engineering fundamentals
- Environmental engineering
- Infrastructure
- Management methods
- Model accuracy
- Models (by type)
- Practice and Profession
- Statistical analysis (by type)
- Traffic accidents
- Traffic congestion
- Traffic engineering
- Traffic management
- Traffic models
- Transportation engineering
- Trees
- Vegetation
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