The prediction of traffic accident duration is great significant for rapid disposal of traffic accidents, especially for fast rescue of traffic accidents and re- moving traffic safety hazards. In this paper, two methods, which are based on artificial neural network (ANN) and support vector machine (SVM), are adopted for the accident duration prediction. The proposed method is demonstrated by a case study using data on approximately 235 accidents that occurred on freeways located between Dalian and Shenyang, from 2012 to 2014. The mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) are used to evaluate the performances of the two measures. The conclusions are as follows: Both ANN and SVM models had the ability to predict traffic accident duration within acceptable limits. The ANN model gets a better result for long duration incident cases. The comprehensive performance of the SVM model is better than the ANN model for the traffic accident duration prediction.
One of the approaches adopted to generate multiclass classifiers from binary predictors is to decompose the multiclass problem into multiple binary subproblems. Among the existing decomposition approaches, one may cite the use of Directed Acyclic Graphs (DAG) to combine pairwise classifiers. This work presents a study on the influence of the DAG structure in the performance obtained in multiclass problems when Support Vector Machines are used in the induction of the binary predictors.
In this paper we present a comparison between the performance of Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs) in a problem of wind speed prediction. Specifically, we analyze the behavior of both algorithms within a larger system of wind speed prediction, formed by global and mesoscale weather forecasting models, and with a final statistical down-scaling process where the MLPs and the SVM are used. The final objective is to forecast the mean hourly wind speed prediction at wind turbines in a wind farm. This is an important parameter used to predict the total power production of the wind farm. The specific model for the short-term wind speed forecast we use integrates two different meteorological prediction global models, observations at the surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model producing the inputs used in the MLP or the SVM, which will forecast the final wind speed at each turbine of the wind farm. In the experiments carried out we compare the results obtained using the MLP or SVM as final steps of the prediction system. Interesting differences of performance can be found when using MLPs or SVMs, which we analyze in this paper. The results obtained are encouraging anyway, and good short-term predictions of wind speed at specific points are obtained with both techniques.