## 1 Introduction  
Compared with the bus-travel mode mode, due to the more punctual and efficient characteristics, the metro-travel mode has, gradually, become the main commuting mode for passengers, such as in Singapore [1], Beijing [2], and Hong Kong [3]. In 2021, Singapore railways (including MRT lines and LRT lines) accounted for 42.8% of passenger volume (source: lta.gov.sg, accessed on 12 April 2022). Beijing rail transit accounted for 55% of passenger volume in 2021, in terms of a report on 24 May 2022 by the Beijing Daily. Hong Kong railways (including MRT Lines, the Airport Express Line, the Light Rail, and HK Tranways) accounted for only 40.1% of passenger volume in 2020, slightly lower than 2019, due to COVID-2019, according to the Statistical Department of Hong Kong Specialiatrics. However, in suburban areas with underdeveloped transportation infrastructure, the metro system is unlikely to reach such a far place. Unlikely, since realizing door-to-door flexible service is one of the main disadvantages of the metro system. Thus, the feeder bus plays an important role in the collection and distribution of metro ridership. Under these circumstances, developing bus-metro-transfer hubs is the key measure to increasing transfer ridership and mitigating the traffic congestion prevalent in big cities today. The development of buses and the metro was interdependent [4]. The rapid development of feeder buses gave full play to the potential positive impact of the metro system and expanded the radiation area of the metro system. Similarly, the development of the metro integrates the public transport network system resources along the metro lines to form a high-quality bus-metro-transfer system. Many previous studies are focused on the bus-metro-transfer hubs [5; 6]. Previous empirical studies indicated that the built environment had a significant impact on bus-metro-transfer ridership [7]. The interaction between the built environment and bus-metro-transfer behavior did not always lead to maximum the attraction of metro. To foster metro ridership around stations, it was important to identify the relationship between the built environment and bus-metro-transfer ridership. Empirical studies on this topic, however, were limited [7]. Many studies using traditional regression models have been devoted to investigating linear associations between the built environment and transit ridership [8; 9]. However, most of them were likely to ignore the pervasive nonlinear and moderating effects among built-environment variables. The influence of the station-area built environment on metro-passenger generation may be influenced by the location and accessibility of stations [10]. For stations with different network attributes, understanding nonlinearity and moderating utility can help designers formulate targeted built-environment strategies. However, only a limited number of studies constructed the impact of network attributes on bus-metro ridership for metro stations [11]. Many other important factors should, also, be incorporated, when conducting the association analysis between bus-metro-transfer ridership and the built environment. In this paper, composed of multiple indicators, we establish a comprehensive index system to analyze the bus-metro-transfer behavior, including bus-metro connection characteristics, land-use attributes for trip attraction and production, network attributes of metro stations, and demographic factors. By comparing with the traditional linear-regression model, random forest, and Light Gradient-Boosting Machine (LightGBM), the extreme gradient-boosting decision-trees (XGBoost) model is chosen to, quantitatively, reveal the nonlinear impact of the built environment on bus-metro ridership. XGBoost is a state-of-the-art machine-learning method, which can, conveniently, reveal the nonlinear or threshold effects of influential factors on bus-metro ridership.