Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: Empirical Analysis

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM), and more recently hierarchical Bayes models (HBM), have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to “over-fitting” the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models were estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the traditional Negative Binomial (NB) regression models. The results of this study show that both types of neural network models perform better than the NB regression model in terms of statistical fit and prediction. Although the BPNN model provides a superior statistical fit than the other two models, its prediction performance is consistently worse than the BNN model, which suggests that the BNN model effectively alleviates the over-fitting problem and has better generalization abilities than the BPNN model. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Language

  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; References; Tables;
  • Pagination: 20p
  • Monograph Title: TRB 86th Annual Meeting Compendium of Papers CD-ROM

Subject/Index Terms

Filing Info

  • Accession Number: 01044938
  • Record Type: Publication
  • Report/Paper Numbers: 07-1542
  • Files: BTRIS, TRIS, TRB
  • Created Date: Feb 8 2007 6:10PM