Citation

Ji X, Huang H, Chen F, Li M. Heliyon 2023; 9(11): e21814.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.heliyon.2023.e21814

PMID

38027797

PMCID

PMC10660521

Abstract

The aim of this study was investigate the spatial effects of A-class scenic spots and the spatial distribution of highway networks on the influence of self-driving tour behavioral patterns in China at the urban agglomeration scale, based on big data of road traffic during three holidays. A spatial analysis method and a geographically weighted regression model were used to analyze the spatial distribution differences and influencing factors of self-driving tourism flows in the central Yunnan urban agglomeration. The results showed that holiday self-driving tourism in the central Yunnan urban agglomeration presented a typical core-edge spatial pattern. The mean value of the spatial autocorrelation coefficient was 0.54, indicating significant spatial autocorrelation. The influence of tourism resources and traffic conditions on self-driving tourism flow showed a decreasing trend from the center of the high positive value to the periphery of the main urban area of Kunming. This study reveals the spatial differentiation characteristics of self-driving tourism flows in urban agglomerations and lays a theoretical foundation for understanding flow pattern.

Language: en

Keywords

Traffic engineering; Geographically weighted regression; Spatial heterogeneity; Tourism transport; Traffic accessibility