Based on railway passenger links, to depict the correlation features of Hang Yong twins and urban agglomeration in Yangtze River Delta, and the geographical scope of the sample cities: Shanghai and all prefecture-level cities in Zhejiang provinces, Jiangsu provinces and Anhui provinces as well as those counties in their railway lines, part cities of Hunan, Jiangxi, Fujian via by the railway extension line, which passes Hang Yong dual cities, as well as a part of the reference cities beyond Hang Yong railway and its extension line, including Shanghai, Nanjing, Hangzhou, Ningbo, Hefei, Changsha, Nanchang, and other cities. Then based on the main railway and its extension line via Hang Yong twins, to determine the sample city unit and site. Sample cities are mainly located in Hang Yong railway line and within the distance of 500 km radius, and parts of sample cities are distributed in 500 - 1000 km railway extension line. What’s more, we introduce a small amount of reference cities. By screening, there are 93 sample cities and 106 sites included in the analysis and measurement at last. Among them, there is one municipality, three deputy provincial cities, 49 prefecture-level cities (including a small area of municipalities), and 40 county-level cities (districts and counties). Sample cities and its provincial domain refer to Table 1.

At present, the main railway lines operated in Yangtze River Delta region include Beijing-Shanghai Railway, Nanjing-Qidong Railway, Shanghai-Hangzhou Railway, Zhejiang-Jiangxi Railway, Xin Chang Railway, the Longhai Railway and other ordinary railway lines, as well as the Shanghai-Nanjing high-speed railway, Shanghai-Hangzhou high-speed railway, Nanjing-Hangzhou high-speed railway, Beijing-Shanghai high-speed railway, Hening part of Shanghai-Chengdu high-speed railway, NingAn intercity railway, railway Ningbo-Taizhou- Wenzhou high-speed railway and other high-speed railway lines. Railways under construction include Shanghai-Nantong railway, Lianyungang-Yancheng railway, Jiangsu coastal railway, Lian-Huai-Yang-Zhen railway, Hangzhou-Huangshan high-speed railway and Shang-He-Hang high-speed railway, etc. High-speed rail lines in Yangtze River Delta region, which have been opened and are about to open by the end of 2016 and the site details are shown in Table 2.

According to method of Zhou Jingxiang [20] and the railway passenger trains, type and the corresponding weights to construct city’s adsorption (Adsorption) and dependency (Dependency) index. Calculation method of city’s adsorption index is

where X_ij is the quantity of trains, j is the weight of trains’ type, according to the importance of different traintypes, the weight is decreasing in turn. I is the ways passenger trains go through the city which include “originator”, “route” and “end to”, and its weight (η_i) is assigned 0.35, 0.3 and 0.35. Calculation method of city’s dependence index:

where A → B means the dependency relationship of city A to city B, and vice versa. X_ijA→B is the quantity of trains which is “originator” and “end to” from city A to Band vice versa; X_ijA˅B is the train from A to B or via A end to B. The assignment of weight and the meaning of other letters are the same as the way of adsorption index.

Note: According to history and publicly reported data sorting summary. Table 2 is the same.

The actual city passenger train data used in this paper is all from the website: Where to go. Where to go provides sites, lines, trains and other information of railway passenger transportation query time between April 20, 2015-May 20, 2015.City site information includes all trains and types of the “originator”, “route” and “end to” in railway stations. After choosing departure and arrival, information of the line shows all trains and types of the “originator”, “route” and “end to”. When making data statistics of city sites and passenger trains between cities, according to the type of trains (G, C, D, Z, T, K, L, O, etc.) to classify, to distinguish the importance of different types of trains in urban linkages. Empirical part involves urban household population, the resident population, GDP, total fixed asset investment as well as its growth rate of the whole society and other variables, whose data is derived from “National Economy and Social Development Statistics Bulletin” and “Government Work Report” of the city over the years. What is more, other data that can’t be obtained is filled by “China city statistical Yearbook” and “regional economic China statistical Yearbook”.

According to the adsorption capacity of sample cities calculated by Formula (1), the cities whose absorption index greater than 20 are Shanghai, Nanjing, Hangzhou, Suzhou, Wuxi and Changzhou, and three cities of the top are Shanghai, Nanjing and Hangzhou, whose city absorption index in order are 62.73, 40.78, and 32.29. Shanghai’s is nearly 20 more than Nanjing’s which ranked second, showing a very strong adsorption capacity of the population. Cities whose absorption index ranges from 10 to 20 include: Xuzhou, Jinshan, Hefei, Zhenjiang, Kunshan, Jiaxing, Jinhua, Ningbo, Yiwu, Bengbu; absorption index ranges from 5 to 10: Danyang, Shaoxing, Wenzhou, Quzhou, Huzhou, Yuyao, Taizhou, Haining, Yixing, Zhuji; absorption index less than 5: Chuzhou Shangyu, Tongxiang, Changxing, Huainan, Wenling, Liyang, Deqing, Quanjiao, Jiashan, Fuyang, Yushan and Songjiang etc. Take Zhoushan as an example of reference points of the city although located in the axis of development (Shaoxing, Huzhou) or have special industries (Zhoushan’s Fishery), the absorption index is 0 due to traffic inconvenience. Table 3 shows the top 30 sample cities and their rank features, and Figure 2 shows their spatial distribution.

According to Formula (2), first measure the dependent pattern between each two cities of the five central cities:

Note: According the results by author’s calculation, then sort out this table.

Shanghai, Hangzhou, Ningbo, Nanjing and Hefei. The dependency index of Hangzhou to Shanghai is 525.1, and Shanghai to Hangzhou is 340.2. The dependency index of Shanghai to Nanjing is 634.3, and Nanjing to Shanghai is 884.55. Likewise, Hangzhou to Ningbo is 239.2, and Ningbo to Hangzhou is 179.3, Hangzhou is most closely related to Ningbo in the five cities, and the following city is Shanghai, whose dependency index to Ningbo is 108.95, and converse is 171.1. It is not difficult to find that there are significant spatial differences in the strength and direction of contact between each two cities of five cities. As shown in Table 4.

Next measure the dependency pattern of sample cities to regional center (Shanghai). Among them, the dependencyof Nanjing to Shanghai is the strongest, followed by four cities: Suzhou, Wuxi, Changzhou and Hangzhou, and their respective dependency indexes to Shanghai in the order are884.55, 769.05, 671.4, 616.15 and 525.1. The dependency index of Ningbo to Shanghai is 171.1, ranking 14^th. These cities, which are not in the Yangtze River Delta and with a longer space distance away from Shanghai, such as Nanchang, Yingtan, Shangrao, Changsha, to Shanghai’s dependency index also enter the top 30. The cities of the top 30 of dependency index gather in Nanjing to Shanghai, Shanghai to Hangzhou, but are relatively sparse between Hang Yongtwins, as is shown in Figure 3. Comparing Figure 2 with Figure 3, it is not difficult to find that there is great overlapin the dependency index between the sample cities and the cities of the top 30 of dependency index. The latter’s concentration degree between Shanghai-Hangzhou, Shanghai-Nanjing and Hang Yong is higher than the former. The top 30 cities of dependency index are shown in Table 5.

Given that city’s absorption index meets with additivity principle, we subtract Hang Yong twins’ adsorption index to hinterland cities, then get the “net effect” that the twins have on their respective hinterland cities. To ma-

Note: According the results by author’s calculation, then sort out this table.

Note: According the results by author’s calculation, then sort out this table.

jority sample cities, Hangzhou’s radiation is more significant than Ningbo’s, and Ningbo’s radiation, only to Shaoxing, Shangyu, Yuyao, Ninghai, Linhai, Taizhou, Shen Fang, Chizhou, Cangnan, Fuzhou, Wenling, Yueqing, Yongjia, Xiapu, Fuding, Ningde, Lin’an, Rui’an and other 19 cities, is relatively strong. These cities, except for Shaoxing, Yuyao and Shangyu, mainly gather in Yong Tai Wen Railway along. Except the cities mentioned above, Ningbo’s radiation to the other sample cities is less significant than Hangzhou’s. The “net effect” which Hang Yong has on their respective hinterland cities is shown in Table 6.

Spatial interaction model shows that, the larger the scale of the urban population, the more potential inter-city

Note: According the results by author’s calculation, then sort out this table. “+” is the Hangzhou’s “Net absorption”, “−” is the Ningbo’s “Net absorption”, the size of the numerical represent the size of the “net absorption”.

population flow. With Shanghai as the center, embedding the dependency index of the hinterland cities to Shanghai, we inversely deduce its “arrival” population scale, then generally judge the hinterland cities’ population scale in deviation, growing space and flow direction, and reveal characteristics of population agglomeration and diffusion between hinterland city and Shanghai.

Dependency index of hinterland cities to Shanghai and its fitting resident population (2014) show strong positive correlation, as is showed in Figure 4,. Taking cities at prefecture level and above of Zhejiang Province and some of the major cities in the Yangtze River Delta as an example, according to the dependency index of hinterland cities to Shanghai, we fit their population scale. By comparing each hinterland city’s fitting population and resident population, we find that, the fitting population scale of Jiaxing, Huzhou, Jinhua, Quzhou, Zhoushan, and Lishui in Zhejiang province, and Nanjing, Wuxi, and Changzhou in Jiangsu province is greater than their actual resident population scale in 2014. To a certain extent, these cities’ population scale is “small”, and there is still some room for its growth. The fitting population scale of Hangzhou, Ningbo, Wenzhou, Shaoxing, Taizhou, Suzhou and Hefei is smaller than the actual resident population scale, revealing better clustering ability. The result is shown in Table 7.

Compare changes on the resident population of cities at prefecture level and above in Zhejiang province since 2006, we find that except for Quzhou, the resident population showed an increasing trend in the rest cities before 2010. The resident population of Hangzhou, Ningbo, Shaoxing, Huzhou, Jiaxing, Jinhua, Zhoushan and Lishui in 2014 increased than that of 2013 Among them, Ningbo showed the largest growth, reaching 14.8 million people, followed by Hangzhou, with an increase of 4.8 million. Hang Yong twins both show great population clustering ability, and Quzhou remains constant, while the resident population of Wenzhou and Taizhou, which take on a greater population absorption ability, has decreased, as is shown in Figure 5.