Abstract
Conventional traffic crash analyzing methods focus on identifying the relationship between traffic crash outcomes and impact risk factors and explaining the effects of risk factors, which ignore the changes of roadway systems and can lead to inaccurate results in traffic crash predictions. To address this issue, an innovative two-step method is proposed and a support vector regression (SVR) model is formulated into state-space model (SSM) framework for traffic crash prediction. The SSM was developed in the first step to identify the dynamic evolution process of the roadway systems that are caused by the changes of traffic flow and predict the changes of impact factors in roadway systems. Using the predicted impact factors, the SVR model was incorporated in the second step to perform the traffic crash prediction. A five-year dataset that obtained from 1152 roadway segments in Tennessee was employed to validate the model effectiveness. The proposed models result in an average prediction MAPE of 7.59%, a MAE of 0.11, and a RMSD of 0.32. For the performance comparison, a SVR model and a multivariate negative binomial (MVNB) model were developed to do the same task. The results show that the proposed model has superior performances in terms of prediction accuracy compared to the SVR and MVNB models. Compared to the SVR and MVNB models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed models for all crash types, and the prediction accuracy that measured by MAPE can be improved by 4.360% and 6.445% on average, respectively. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Furthermore, the results show that the proposed model has a more precise and synchronized response behavior to the high variations of the observed data, especially for the phenomenon of extra zeros.
Highlights
Traffic crashes result in countless fatalities, injuries, many dollars expenses in medical and property lost
Compared to the support vector regression (SVR) and multivariate negative binomial (MVNB) models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed SSM-SVR models for all crash types, and the prediction accuracy that measured by Mean Absolute Percentage Error (MAPE) can be improved by 4.360% and 6.445% on average, respectively
With the traffic flow as the control input, the system state is changing by time and the effects of impact factors on roadway system were captured by the state variables
Summary
Traffic crashes result in countless fatalities, injuries, many dollars expenses in medical and property lost. The results showed that the traffic characteristics have greater effects on crash frequency and severity compared to the geometric variables. The results showed that back-propagation neural network (BPNN) and BNN models outperform the NB regression models in terms of traffic crash prediction. Roadway traffic crash prediction modified ANN models including the studies of Huang et al [32] and Zeng et al [33, 34] confirmed that the modified ANN models have better performances compared to the statistical models. The results showed that the SVM models predict crash data more effective and accurate than traditional NB models. It has been found that the SVM models showed better or comparable results to the outcomes predicted by ANN/BNN and other statistical models. To validate the model effectiveness, a five-year dataset that obtained from 1152 roadway segments in Tennessee was employed and a SVR model and a multivariate negative binomial (MVNB) model were developed as the benchmark methods
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