融合语义向量与微链结构特征的购物出行模式识别OA

To better capture the characteristics of urban residents'travel behavior,such as the connections between pre-and post-activities,travel modes,and key time nodes,this paper constructs a"micro-chain"structure centered on shopping activities and proposes a shopping travel pattern recognition framework that integrates structured travel features with semantic vectors.The framework leverages Word2Vec and SIF to encode the semantic information of entire travel chains and combines this with time encoding and activity sequence features.Using the K-means++clustering method,it uncovers latent behavioral patterns in shopping-related travel chains of urban residents.Applying the framework to shopping travel data from Shanghai residents,five distinct shopping travel patterns with significant differences are identified.Comparative analyses show that the proposed method outperforms traditional travel attribute-based encoding approaches in both clustering clarity and interpretability.This paper provides a fine-grained perspective on urban shopping travel behavior and offers a transferable feature construction and clustering framework for urban travel behavior research.