李晓伟 Likes:

Impact Factor:

1.6

Affiliation of Author(s):

西安建筑科技大学

Journal:

Journal of Transport and Land Use

Key Words:

Travel distance; Bus transit systems; Built environment; Medium-sized cities; Machine learning; Big data

Abstract:

The impact of the built environment and weather conditions on travel behavior has been widely studied. However, limited studies have focused on better understanding such effects in medium-sized cities with bus-oriented transit systems, particularly from a separate perspective of travelers’ origins and destinations. We took Weinan, China, as a representative of second-tier cities in developing countries that concentrate on bus-oriented development strategies. New evidence of feature importance and nonlinear effects of crucial factors were revealed by an interpretable machine learning-based approach combining XGBoost and Shapley Additive Explanation (SHAP) with multi-source data. Most key factors were critical at both origins and destinations, such as the density of residential and commercial facilities. However, several important factors, such as road density and boarding time, had strong imbalanced effects on travel behavior. These findings provide novel insights and empirical implications to support urban planning strategies in medium-sized cities.

Co-author:

Shi Lanxin,Tang Junqing,Zhao Pengjun,Li Jiaying,Liu Qian,Chen Jun,Ma Changxi

First Author:

Li Xiaowei

Indexed by:

Journal paper

Discipline:

Engineering

First-Level Discipline:

Communication and Transportation Engineering

Document Type:

J

Translation or Not:

no

Date of Publication:

2024-01-01

Included Journals:

SSCI