Modeling truck accidents at highway interchanges: Prediction models using both conventional and artificial intelligence approaches: Regression, neural networks, and fuzzy logic

Large trucks represent a significant proportion of overall vehicle volumes on the nation's highways, and this proportion is increasing at the same time that larger and longer trucks are being used. Highway geometric design elements, including interchange configurations and ramp characteristics, contribute significantly to traffic accidents that involve trucks. However, this contribution is very difficult to quantify, because of the confounding influence of other factors, such as human behavior, traffic conditions, and prevailing weather conditions.; Most previous accident studies used regression analysis to develop equations to explain accident rates. All previous attempts have had mixed results, and no set of geometric/accident relationships is widely accepted. Deficiencies of such models were attributed to different factors, such as quality and quantity of accident data and statistical methods used for prediction.; Accident reporting systems in most states compile information about many variables that contribute to accident causation in a non consistent way. For example, for two different accidents, "wet surface" can be a contributing factor to one accident, but only a neutral factor in another accident. Existing accident reporting systems do not solve this problem.; In this study, different approaches were applied to explain truck accidents at interchanges in Washington state during the period from 1/1/1993 to 3/3/1995. Three models for each ramp type were developed using linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was only 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 linear regression. However, the ability of these models to predict test data that was not included in the training process showed unsatisfactory results. The results suggest that AI approaches are promising tools for exploring the problem, but that the data have many deficiencies.