Research on intercity travel mode recognition and network structure characteristics based on complex network and random forest classification.

ABSTRACT: This study aims to address the issue of identifying diverse intercity travel modes in high population density areas and analyzing their network structure characteristics over different time periods. Consequently, a management model combining complex network theory (CNT) with a random forest classification (RFC) algorithm-referred to as the CNT-RFC model-is proposed. The study utilizes publicly available migration data from the National Bureau of Statistics and transportation departments from January 2021 to December 2023. Network structure features are extracted through node degree distributions, edge connections, centrality metrics, and community detection algorithms. Integrating RFC enables precise identification of travel modes and uncovers the spatio-temporal heterogeneity of intercity

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12511360 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature