Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Abstract

Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper. The selection of parameters greatly affects the prediction accuracy of support vector machine. Introducing particle swarm optimization can find the optimal parameters and improve the prediction accuracy of support vector machine by parameter optimization. However, standard particle swarm optimization is easy to trap into the local optimum, so that the best parameter solutions cannot be found. Therefore, the mutation operation of the genetic algorithm is introduced into particle swarm optimization, particle swarm with mutation optimization is generated. It expands the search space and makes parameter selection more accurate. This paper predicts fatalities of traffic accident using small samples and nonlinear data. The results show that compared with particle swarm with mutation optimization back propagation neural network prediction model, particle swarm optimization-support vector machine model, support vector machine, back propagation neural network, K Nearest Neighbor (K-NN), and Bayesian network, the prediction model of traffic fatalities based on particle swarm with mutation optimization-support vector machine has higher prediction precision and smaller errors. It is feasible and effective to use particle swarm with mutation optimization to optimize the parameters of support vector machine, and this model can predict the accident more accurately.

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

“…In this study, the ANN model compared with the proposed Hybrid model. The Comparative performance of both models showed that the proposed model (Hybrid K means and random forest) performed better than the ANN model in terms of Precision, Recall, F1 score, Gu et al [21] PSO-SVM China -Xiao et al [52] SVM, KNN (Ensemble) I-880 data set 99.33% Castro et al [15] BN, JR8 and MLP DVSA-UK 72.39%, 72.02%, 71.70% Respectively Al-Radaideh et al [4] RF, ANN (backpropagation), SVM Uk 80.6%, 61.4%, 54.8% respectively Casado et al [14] LCC, MNL Spain -Wahab et al [51] MLP. SimpleCart, PART Ghana 72.16%, 73.45%, 73.81% respectively Sameen et al [40] MLP, BLR, RNN Malaysia 65.48%, 58.30%, 71.77% respectively Fentahun [18] J48, ID3, PART Ethiopia 81.21%, 81.01%, 81.18% Seid et al [42] HMR Ethiopia NA Abebe et al [1] DSA Ethiopia -Lytin et al [30] UBA Ethiopia - and Accuracy.…”

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

“…Numerous impediments related to the statistical analysis of crash data remain [46]: the need to satisfy some statistical hypotheses [47] or the difficulty in managing several variables with many categories [48,49]. To overcome the deficiencies of these methods, road safety researchers have proposed new Non-Parametric Models [50], such as the Classification and Regression Tree (CART), widely used for the analysis of crash outcomes [51][52][53][54], and the Support Vector Machine (SVM) models which are normally utilized for the classification of crash injury severity [55][56][57][58][59]. Recently, Artificial Neural Network (ANN) has also been used to carry out the classification of crash severity and their applications have grown extraordinarily [60][61][62][63][64].…”

Application of deep learning techniques in predicting motorcycle crash severity

“…6,13,14 Researchers also found the better prediction performance of the support vector machine (SVM) in developing crash injury severity models compared to other parametric models. [15][16][17] Moreover, despite limited application in transportation sector, multivariate adaptive regression splines technique has outstanding predictive power in crash injury analysis. [18][19] DL has recently become the new focus in transportation safety analysis.…”

“…For instance, Random Forest Model is a promising data mining approach employed in many studies to prioritize the variables associated with crashes 6,13,14 . Researchers also found the better prediction performance of the support vector machine (SVM) in developing crash injury severity models compared to other parametric models 15–17 . Moreover, despite limited application in transportation sector, multivariate adaptive regression splines technique has outstanding predictive power in crash injury analysis 18–19 …”