Spatial Patterns Of China’s Urban Network From The Perspective Of Multiple Passenger Flows

Traffic flow acts as major carrier of other flows(including people, goods, capital, etc.), thus the study on traffic flow is of great significance to understand intercity interaction. Based on the 321 cities at prefecture level or above, this article aims to reveal the spatial pattern of urban external connection intensity from the respective of traffic flow and explores spatial linkage of China’s city network using intercity linkage data of road, rail and air transport.Previous urban studies on traffic flow laid more emphasis on single type of traffic flow while multi-flow synthesis was not taken into account. Moreover, the scale of prefecture-level city was not be covered before. Therefore, a further analysis on spatial pattern of multi-type traffic intensity between cities in China is required. To detail and comprehensively consider the spatial pattern of cities in China, we narrow the study scale to prefecture-level city. According to the list of prefecture-level divisions of China, this paper firstly takes 321 cities as basic study units. Data crawling was implemented under the C# language environment, which collects the runs number of three transport modes as basic data. Then we measure external connection index of each city respectively through bus, railway and flight schedules. In the following analysis, kernel density estimation was employed to describe spatial distribution pattern of urban external connection intensity, which implies a concentration trend. The number of runs shows bus > train > flight. Furthermore, Rank-size Rule was applied to portrait distribution variations of urban external connection intensity, among them flight schedules show significant rank-size feature, while train schedules come last. From the perspective of the whole urban system, the distribution of urban size is concentrated. Specifically, top-ranking cities have considerable scales, while small and medium-size cities need further development.After that, we use ESDA(Exploratory Spatial Data Analysis) to examine the spatial agglomeration. There is apparently positive spatial correlation between urban external connection index based on bus schedules data and that based on train schedules data. Spatially, a gradually weakening trend appears from coastal to inland. At the same time, core-periphery structure can be recognized, which identifies the national railway artery as core area and areas along the railway as periphery. To be noticed, spatial distribution based on flights shows a highly polarized, spot-shaped embedded feature, and regional balance remains to be further optimized. In addition to the above features, the intercity traffic flow intensity based on road linkage mainly shows spatial dependence, while the intercity traffic flow based on railway and flight linkage demonstrates spatial heterogeneity to some extent.The research results also show that:(1) The spatial correlation of city network based on road linkage displays strong spatial dependence, which is very useful to identify urban agglomerations and assess developments. Besides identifying the most developed regions including Yangtze River Delta, Pearl River Delta and Beijing-Tianjin-Hebei urban agglomerations, some important urban agglomerations have emerged, such as Zhongyuan, Harbin-Daqing-Qiqiha’er, Central Jilin, Mid-southern Liaoning, Shandong Peninsula, Guanzhong, West coast of Taiwan Strait, Wuhan, Central Yunnan, Changsha-Zhuzhou-Xiangtan, etc.(2) The railway linkage flow reflects intercity external connection pattern, regional element relevancy and regional accessibility along with national railway artery. “Two horizontal and three longitudinal” zonal distribution pattern composed of Beijing-Guangzhou, Beijing-Harbin, Beijing-Shanghai Railway and Longhai-Lanxin, Shanghai-Kunming Railway, constitutes the urban network backbone and become the most important economic axial belts for national territorial development.(3) The “diamond structure” as the core framework basically forms the skeleton of urban network system from the perspective of air passenger flow, whose vertices are Beijing, Shanghai, Guangzhou-Shenzhen and Chengdu-Chongqing. Generally speaking, different types of traffic flows reflect different patterns of intercity linkage, while there also exist internal relationship. Air passenger flow constitutes the backbone of intercity linkage pattern, railway linkage flow acts as supporting axis belts for the core framework, and road linkage is a bridge connecting main skeleton and supporting belts. All types of traffic flows collectively form interdependent and indispensable element correlation and spatial relationship among regions. In this paper, we focus on the spatial pattern of intercity traffic flow intensity under the current single year. And it is of great significance to understanding the evolution process and formation mechanisms of intercity networks based on multi-type traffic flows using time series data.