Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques
| Autor: | Wael H. Awad, Bruce N. Janson |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Engineering
Variables Artificial neural network business.industry Mechanical Engineering media_common.quotation_subject Poison control Regression analysis Fuzzy logic Standard deviation Linear regression Artificial intelligence business Predictive modelling Civil and Structural Engineering media_common |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 1635:30-36 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.3141/1635-04 |
| Popis: | Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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