A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction

Traffic accidents causing nonrecurrent congestion can decrease the capacity of highways and increase car emissions. Some models in previous studies have been built based on artificial intelligence or statistical theory because estimating the duration of an accident can aid traffic operation and management. However, only characteristics of traffic accidents were considered in most models; the spatial-temporal correlations of traffic flow were always ignored. In this study, a deep fusion model, which can simultaneously handle categorical and continuous variables, is proposed. The model considers not only the characteristics of traffic accidents but also the spatial-temporal correlations in traffic flow. In this model, a stacked restricted Boltzmann machine (RBM) is used to handle the categorical variables, a stacked Gaussian-Bernoulli RBM is used to handle the continuous variables, and a joint layer is used to fuse the extracted features. With extracted 1-80 data, the performance of the proposed model was evaluated and compared to some benchmark models. Furthermore, the target variable (duration) was divided into ten groups, and then the evaluation criteria of the models of each group were calculated. The results show that the novel model outperforms some previous models and that the fusion of different types of variables can improve prediction accuracy. In conclusion, the proposed model can fully mine nonlinear and complex patterns in traffic accident data and traffic flow data. The fusion of features is important to predict traffic accident durations.