Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM

Abstract

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the risk of railway dangerous goods transportation system (RDGTS). The statistical occurrence frequency of each sub-risk indicator of the happened RDGTS accidents is used as the basis of experts’ scores, the experts’ scores are presented in interval numbers form, which are used as the inputs of the four approaches. The accuracy rate, optimization time consuming, Mean Square Error (MSE), Receiver Operating Characteristic Curve (ROC) and Area Under Curve (AUC) are used as the evaluation indexes of the identification results. The comparison studies are conducted by using SVM with linear kernel (SVM-L) and SVM with polynomial kernel (SVM-P), respectively. By using such new methodology, the risk identification problem (evaluation problem) is transferred into a classification problem with faster identification speed, higher identification efficiency and higher accuracy. The identification results show that: GS-SVM is the optimal approach to identify the risk factors of Human; SVM is the optimal approach to identify the risk factors of Machine, Materials, Environment and Management. SVM has the shortest optimization time consuming, GA-SVM has the highest accuracy, hence, SVM and GA-SVM are better to applied to identify the risk of RDGTS. The optimization time consuming of all models is no more than 5 s, which means the RDGTS risk identification results could be obtained fast and high-efficiently and the mental strength for researchers can be reduced by using the SVM and improved SVM models. For the risk identification results considering MSE and AUC, GS-SVM is the most accurate and best classification algorithm, and the results based on SVM-P is better than the results based on SVM-L. The results based on PSO-SVM, GA-SVM and GS-SVM have better accuracy and reliability than that based on SVM-L or SVM-P, which means the PSO-SVM, GA-SVM and GS-SVM based risk identification approaches are practicable.