Railway Safety Assessment Model Solving by Decision Tree and PSO Algorithm

Abstract

Abstract Safety assessment and safety warning in intelligent railway play an important role in safety management. This paper utilizes the PSO algorithm with bounds and time-varying attractor to obtain some parameters in the safety assessment model. Firstly, the safety assessment model is provided to evaluate the safety status on the existing problem, railway equipment and railway worker. Secondly, one objective function and three constraint conditions on training the safety assessment model are introduced to minimize the square error between the output and the safety classification level. Thirdly, it is important to discuss the convergence behavior and spectral radius on transfer matrix in the PSO algorithm with bounds and time-varying attractor. Finally, in order to show and demonstrate the effectiveness of the PSO algorithm, it is important to analyze the objective fitness, the swarm velocity, the time-varying attractor, the parameters in the safety assessment model and the computational time during the evolutionary process. And the PSO algorithm with bounds and time-varying attractor can solve parameter optimization problem on the railway safety assessment and warning.

Keywords

• Intelligent railway
• Safety assessment
• Particle swarm optimization
• Convergence analysis
• Spectral radius