How Rail Transit Makes a Difference in People’s Multimodal Travel Behaviours: An Analysis with the XGBoost Method

Abstract

The rail transit system was developed in Chinese large cities to achieve more efficient and sustainable transport development. However, the extent to which the newly built rail transit system can facilitate people’s multimodality still lacks evidence, and limited research examines the interrelationship between trip stages within a single trip. This study aims to explore the interrelations between trip stage characteristics, socio-demographic attributes, and the built environment. It examines how rail transit is integrated as part of multimodal trips after it is introduced. The data are extracted from the Chongqing Urban Resident Travel Survey from 2014, three years after the new rail transit network was established. It applies an XGBoost model to examine the non-linear effect. As a result, the separate trip stage characteristics have more of an impact than the general trip characteristics. The non-linear effects revealed by the machine learning model show changing effects and thresholds of impact by trip stage characteristics on people’s main mode choice of rail transit. An optimal radius of facility distribution along the transit lines is suggested accordingly. Synergistic effects between variables are identified, including by groups of people and land use characteristics.