**4. Discussion**

#### *4.1. Railway Network Distribution in Urban Agglomerations*

The distribution structure of urban agglomerations according to the "5 + 9 + 6" plan is shown in Figure 6, in which pink areas represent five national-level urban agglomerations, yellow areas represent nine regional-level urban agglomerations, and green areas represent six local-level urban agglomerations.

**Figure 6.** Distribution structure of urban agglomeration following the "5 + 9 + 6" plan of China in 2015.

Based on this distribution structure, the railway network distribution index in each urban agglomeration can be calculated (see Figure 7). The railway network distribution indices range

from 0.16 to 0.41 for all urban agglomerations, indicating relatively lacking, moderate, and good distributions according to the spatial patterns of railway network distribution discussed in Section 3.4. The five national-level urban agglomerations all have moderate railway network distributions, which indicates that a moderate network distribution should continue to be a focus for supporting the fastest urbanization in national-level urban agglomerations [40,41]. Of the nine regional-level urban agglomerations, Liaozhongnan, Jianghuai, Harbin-Changchun, and the Central Plains have good railway network distributions; the west side of the Straits, Shandong Peninsula, Guanzhong Plain, and Beibu Gulf have moderate railway network distributions; and Tianshan North Slope has a relatively insu fficient railway network distribution. The construction of railway network facilities should be increased as an important driving force for urbanization in these areas [42]. Of the six local-level urban agglomerations, only Qianzhong has a relatively insu fficient railway network distribution, and the others have a moderate railway network distribution. These local-level urban agglomerations are mainly located in western China, with relatively low urbanization levels and a moderate railway infrastructure that could support the economic development in these areas [43].

Overall, the railway network distribution in internal urban agglomerations is not perfect; only four regional-level urban agglomerations have good railway network distributions, while the rest have medium or relatively insu fficient railway network distributions. Therefore, in order to meet China's urbanization needs, as well as match the development of the population and economy, it is better to focus on strengthening the construction of railway network within the national-level urban agglomerations.

**Figure 7.** Railway network distribution index in each urban agglomeration in 2015.

#### *4.2. Railway Network Distribution in Four Economic Regions*

The four major economic regions of China are shown in Figure 8. The main features of economic and social development in the various regions are the development of the western region, the revitalization of the northeast region, the rise of the central region, and the leading development in the eastern region.

The railway network distribution index is 0.31 in the eastern region, 0.32 in the central region, 0.19 in the western region and 0.39 in the northeast region. The railway network distribution of the northeast region is the best, followed by those of the central and eastern regions, while the western region is the most insu fficient. Wang et al. (2009) divided China's railway network expansion over the last century into four eras: preliminary construction, network skeleton, corridor building, and deep

intensification [7]. Prior to 1950, the railway network mileage of the northeast region accounted for more than 40% of the network in China. In the 1970s, China implemented the policy of "reform and opening up" and shifted the economic center and railway construction to the southeast coastal region. During this period, the railway network in the eastern coastal region developed rapidly, which led to unbalanced inter-regional development. In the late 1990s, China implemented balanced regional development and successfully put forward the strategies of "the grea<sup>t</sup> western development strategy," "revitalizing the old industrial bases in northeast China," and "the rise of the central region", which gradually enhanced the construction of the railway network in the central and western regions. In 2015, China proposed "The Belt and Road" development strategy, which provides targeted guidance for the future distribution of the railway network.

Therefore, according to the national development strategy, the future distribution of the railway network could focus on the central and eastern regions as well as the western urban agglomerations, while the western and northeast border areas may mainly consider their national security significance. A logical distribution of the railway network will guide urbanization and the development of population and economic aggregation.

**Figure 8.** The four major economic regions of China.

#### *4.3. Limitations and Future Improvements*

This study mainly considers railway passenger transport, and represents the size of railway passenger transport with train frequency data. Due to the di fficulty in data acquiring, we do not take into account the freight volume situation of China in this study. Adding the freight transport information will definitely make the railway network distribution pattern more accurate.

Lim and Thill (2008) investigated how the intermodal freight-transportation network a ffects the ability of regions to position themselves more e ffectively in the national space economy, and the performance of the intermodal freight network was evaluated by comparing accessibility measures based on the highway network and on the intermodal network, respectively [44]. This research can provide reference and guidance for the future research for combining passenger and freight status, and reflecting the characteristics of the railway network distribution of China more precisely.

In this study, the distance from the nearest railway station to the county center, based on the provincial-level functional area division, is used to determine the weight of railway network proximity using expert scoring. Potential future improvements could include setting di fferent distance intervals and selecting di fferent evaluation methods for expert scoring. In addition, the current research has only been applied to examine railway network distribution at the macro scale. The theory and evaluation method needs further testing if being applied to regions at small—and medium-scales.
