Front-end speech processing system with SVM algorithm in rail passenger flow management

Article type: Research Article

Authors: Li, Ningchuan

Affiliations: Rail Transit College, Chengdu Vocational and Technical College of Industry, Chengdu, Sichuan, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: Rail Transit College, Chengdu Vocational and Technical College of Industry, Chengdu, Sichuan, China. E-mail: [email protected].

Abstract: In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on front-end voice processing technology and support vector machine models. The study first conducted a railway passenger flow analysis, and then used a support vector machine model to construct a preliminary prediction system. In order to achieve global optimization, the study also introduced particle swarm optimization algorithm to construct an optimization prediction model based on PSO-SVM. The results show that the proposed PSO-SVM method has undergone 48 iterations of training, and the predicted values closely match the actual passenger flow curve. The maximum RE error is 2%, and the overall prediction error is 98%. The decision coefficient of PSO-SVM is 0.998932. Therefore, this indicates that it has high performance and feasibility in predicting and controlling passenger flow during peak hours of urban rail transit.

Keywords: Urban rail transit, passenger flow, support vector machine, genetic algorithm, particle swarm

DOI: 10.3233/JCM-247338

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1173-1187, 2024