Elevator Traffic Pattern Recognition Method Using Particle Swarm K-means Clustering Algorithm

A method for elevator traffic pattern recognition based on Particle Swarm Optimization (PSO) enhanced K-means clustering algorithm. The approach performs cluster analysis on raw passenger flow data from the previous week to obtain cluster center coordinates representing different traffic patterns[2]. For real-time traffic flow data, passenger statistics are collected in 5-minute intervals and assigned to the nearest cluster center using nearest neighbor principles, enabling identification of current traffic modes. Implementation typically involves PSO optimization of cluster centroids and Euclidean distance calculations for pattern classification.