基于聚类算法的高铁旅客市场细分方法研究OA

To address the instability and difficulty in effectively extracting characteristic differences among passenger groups when applying traditional clustering algorithms to high-speed rail passenger market segmentation,this study proposes a K-Means-Adaptive Learning Particle Swarm Optimiza-tion(KM-ALPSO)algorithm based on the Halton sequence.This algorithm is applied to segment the high-speed rail passenger market using ticket data from the Beijing-Shanghai high-speed railway.First,considering passenger age,advance booking time,and departure period as feature variables,the Affinity Propagation(AP)algorithm is adopted to identify representative sample points within the dataset.Second,an initial particle swarm is generated based on the Halton sequence,and the KM-ALPSO algorithm is used to cluster the representative sample points.A comparative analysis is then conducted against the classic K-Means algorithm and the K-Means-Particle Swarm Optimization(KM-PSO)algorithm.The Silhouette Coefficient(SC),Davies-Bouldin(DB)index,and Calinski-Harabasz(CH)index are selected to evaluate the clustering performance and determine the optimal number of clusters.Finally,the characteristic differences among various passenger groups are ana-lyzed,and the Frequent Pattern-Growth(FP-Growth)algorithm is employed to extract strong associa-tion rules.The results indicate that preprocessing with the AP algorithm reduces the runtime of the Halton sequence-based KM-ALPSO algorithm to 23.26%of its original duration while maintaining evaluation metrics comparable to those obtained without preprocessing.Furthermore,initializing par-ticle swarm positions with the Halton sequence enhances the global search capability of the KM-ALPSO algorithm.In the analysis of ticket data,the Halton sequence-based KM-ALPSO algorithm achieves an SC of 0.332,a DB of 0.933,and a CH of 708.5,demonstrating that a cluster number of five yields optimal performance and outperforms the baseline algorithms.The five identified passenger groups exhibit significant differences in age,advance booking time,and departure period,revealing distinct travel planning tendencies and departure time preferences.