Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach

Yifeng Ren (), Min Yang (), Enhui Chen (), Long Cheng () and Yalong Yuan ()
Additional contact information
Yifeng Ren: Southeast University
Min Yang: Southeast University
Enhui Chen: Southeast University
Long Cheng: Southeast University
Yalong Yuan: Southeast University

Transportation, 2024, vol. 51, issue 4, No 12, 1493-1523

Abstract: Abstract The transfer city is a key point in air-to-rail intermodal travel (ARIT) that directly influences the service level of the entire system. Although some studies have investigated factors that influence passengers’ ARIT preferences based on subjective surveys, an in-depth understanding of their nonlinear and interactive impacts on passengers’ actual behavior is still lacking. Using passengers’ online booking data in China, this study implements an interpretable ensemble machine learning framework that incorporates decision-making theory to unveil feature importance and the complex nonlinear and interactive effects of various attributes on passengers’ choice of transfer city in the crucial ARIT scenario. The results show that (1) the extreme gradient boosting (XGBoost) model achieves better performance in predicting ARIT transfer city choice than the conventional discrete choice model; (2) attributes related to intermodal services (e.g., ticket price, in-vehicle duration, transfer duration, quantities of flights and trains) are more important than personal demographic characteristics (e.g., age and gender); (3) factors related to service economy and efficiency display nonlinear impacts with fluctuations and critical thresholds; and (4) individuals with different characteristics present heterogeneous preferences for ARIT transfer cities. These findings can provide useful managerial implications for policymakers.

Keywords: Air-to-rail intermodal travel (ARIT); Transfer city choice; Interpretable machine learning methods; Extreme gradient boosting (XGBoost) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11116-023-10375-3 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-023-10375-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11116/PS2

DOI: 10.1007/s11116-023-10375-3

Access Statistics for this article

Transportation is currently edited by Kay W. Axhausen

More articles in Transportation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().