## 2 Methodology
### Study Data  
In 2018, a governmental report [44] outlined plans to advance the integrated development of key national and regional strategies within various major city clusters in China's forthcoming urban landscape. These regions encompass the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Heibei, Chengdu-Chongqing, Mid-Yangtze River, Central Plain, and Guanzhong Plain. This study focuses on seven highly representative urban agglomerations in China as the research area.  
The data pertaining to county-level administrative areas are predominantly incomplete, less reliable, and challenging to access. This study primarily aims to analyze the efficiency of intercity transportation networks within prefectural cities across different city clusters.  
The study gathers data at the prefectural city level and subsequently consolidates it to the level of city clusters. The primary data utilized comprise economic and social indicators, as well as the intercity travel time between each pair of cities.  
Economic and social data are gathered from sources such as the China City Statistical Yearbook, China Regional Economic Statistical Yearbook, as well as provincial and municipal statistical yearbooks. The travel time between two cities by car is acquired through the Amap route planning module via API.  
To calculate the intercity travel time, the initial step entails identifying the location information. Urban agglomerations involve a large area with a large number of city pairs. It is impractical to visit a large number of points within each city to establish mobility OD data. Secondly, train travel time necessitates the use of the National Railway Customer Service Website, which involves a considerable workload. Thirdly, acquiring travel data in certain underdeveloped areas of prefecture-level cities presents challenges, with the possibility of encountering a scarcity of accurate satellite maps. Therefore, following a review of the relevant studies [5, 38], this paper selects the seat of the city government as one of the criteria for determining OD points.  
In order to further supplement, we choose pinpointing the area within the city with the highest concentration of points of interest as another criterion for OD points, denoted as point E. This represents the bustling economic center of the city.  
Following this, two modes are chosen--government-government and point E-point E--which symbolize the linkage between the political and economic hubs of each city and their respective counterparts in other cities. These two OD standards define the general area. This particular area accommodates the highest population density in the city and, thus, represents intercity travel for the majority of city residents.  
The dataset analyzes the regular travel time patterns of intercity travel, focusing on the shortest travel times between 2686 city pairs by both train and car. Data are collected hourly from 8:00 a.m. to 8:00 p.m. during the entire month of April 2023, for the selection of time nodes. Two configurations of starting and ending points are examined across a 30 d period. The average travel time for each city pair in the city cluster is calculated based on these two configurations, leading to the creation of a comprehensive travel time matrix encompassing all city pairs within the cluster. It is important to highlight that the train travel time matrix and the car travel time matrix are computed independently.