Design and evaluation of high-speed train head shape based on data-driven analysis

A systematic study was conducted on the design concept and evaluation methods of high-speed train head shapes using a convolutional neural network algorithm and the maximal information coefficient (MIC) principle. Through data-driven system analysis, the key factors influencing the design of high-speed train head shapes were identified, and an evaluation index system was established. The index values were quantified using the MIC principle, thereby establishing a comprehensive evaluation model for high-speed train head shapes. The established model was used to evaluate and analyze the head shapes of Japanese high-speed trains, including the 0, 100, 700, and 800 series, as well as the E5 and ALFA-X types. Results show that the ALFA-X train achieves a comprehensive score of 1.835, indicating superior operational performance that aligns with the actual operational data. Therefore, the data-driven evaluation index system for high-speed train head shapes presented in this paper accurately reflects the current performance status of train heads.