## 1 Introduction
[25] proposed a data-driven methodological framework based on smart card data \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Serving Area** & **Designed/(km/h)** & **Distance Between** & **Line** \\  & & **Speed/(km/h)** & **Stations/km** & **Length/km** \\ \hline Urban rail transit & Urban areas of cities & 80–100 & 0.5–1 & \textless{}40 \\ Intracity railway & Connecting urban & 100–160 & 3–7 & 30–80 \\ Intercity railway & Metropolitan areas & 120–200 & 5–20 & \textgreater{}100 \\ \multirow{2}{*}{Trunk railway} & \multirow{2}{*}{General railway} & Long-distance travels & \textless{}160 & 10–50 & \textgreater{}300 \\  & & in the country & 250–350 & 30–60 & \textgreater{}300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main indicators of multi-level rail transit. for estimating transfer ridership, and through the case of the Shenzhen metro network, it verified its effectiveness. All of these empirical studies have shown that smart card data are feasible in travel patterns research. With the popularization of the intercity metro, the travel patterns of intercity metro ridership can be analyzed based on smart card data. Therefore, this paper is based on Changsha metro smart card data to explore the travel patterns. In general, although numerous studies have discussed the travel patterns of intercity railway ridership, there are still some weaknesses. Firstly, most existing studies focus on trunk or intercity railways, with limited research on intercity URTs, which have very different technical features. However, considering the intercity URT system has increased quickly in the past decade in China, it is time to conduct the specific research on their performance now. Secondly, the existing research on intercity travel mainly relies on data from questionnaires, mobile signaling data, etc., which cannot collect the precise time and space information of travelers. However, the AFC data have the precise travel information of passengers, which can be used to figure out their travel patterns clearly and efficiently. Finally, most existing studies cluster intercity passengers from single dimensions, and few of them consider the stay time modes of intercity passengers. But the unique characteristics of stay time modes are important to fully understand the intercity travel patterns. Aiming at these issues, this study is designed to comprehensively and precisely explore the travel patterns of intercity URT passengers to further optimize their services and efficiency. Specifically, it focuses on analyzing the differences between weekday and weekend travel patterns, addressing the gap in understanding intercity URT travel behaviors that currently limits service optimization. Therefore, using smart card data from the Changsha-Zhuzhou-Xiangtan (CZT) Intercity Metro Xihuan Line in China, this study applies the GMM to cluster travel time and stay time. It analyzes ridership characteristics from two perspectives: metro travel behavior and urban stay patterns, providing more comprehensive decision support for metro operations and urban management. The following paper is organized as follows: Section 2 introduces materials adopted in this study; Section 3 introduces the methods adopted in this study; Section 4 further clusters intercity ridership with the GMM based on travel time and stay time; Section 5 gives conclusions.