李晓伟 Likes:

Journal:

Applied Geography

Key Words:

Intercity travel; Machine learning; Mode choice; Nonlinear effect; XGBoost

Abstract:

Unraveling the complex relationships between intercity travel mode choices and their determinants is valuable for transport planning and development. However, a better understanding of the key influential factors shaping passengers' comprehensive mode choices for intercity travel, as well as the interactive and nonlinear effect between these two, is still needed. By conducting field surveys in Xi'an, China, a tourist city, this study collected intercity travel mode data for airplanes, high-speed railway (HSR), traditional trains, and express buses, as well as a comprehensive spectrum of passengers' attribute factors, including relatively underexplored factors such as online ticketing methods. The XGBoost algorithm and SHAP analysis were applied to conduct an in-depth and interpretable effect diagnosis. The results reveal obvious interactions among passengers' socioeconomic and travel behavior attributes. Among them, we find that online ticketing methods play an indispensable role in different population groups. Additionally, travel distance has the most significant nonlinear effects on intercity multimodal choices; this demonstrates a highly heterogeneous influential effect and a higher sensitivity than for intra-city travels. This study provides new evidence to better understand intercity travel behaviors and provides policymakers with targeted strategies for regional and national intercity transport planning and management.

Co-author:

石兰馨,师洋,汤俊卿,赵鹏军,王玉婷,陈君

First Author:

李晓伟

Indexed by:

Journal paper

Document Type:

J

Translation or Not:

no

Date of Publication:

2024-01-01

Included Journals:

SSCI