Research on the Coupling Coordination Relationship between Urban Rail Transit System and Sustainable Urban Development
Abstract
:1. Introduction
2. Methods
2.1. Entropy Weight Method
- (1)
- Establishment of an evaluation matrix
- (2)
- Normalization of all indicators
- (3)
- Calculation of the entropy
- (4)
- Calculation of the weight
2.2. TOPSIS Method
- (1)
- Construction of a weighted decision matrix
- (2)
- Calculation of the positive ideal distance and the negative ideal distance
- (3)
- Calculation of relative closeness
2.3. Coupling Coordination Degree Model
- (1)
- Calculation of coupling degree
- (2)
- Calculation of coordination degree
- (3)
- Calculation of coupling coordination degree
2.4. Grey Relational Analysis
- (1)
- Determine the reference sequence and comparative sequences
- (2)
- Normalize the values of the original sequences
- (3)
- Calculate the grey correlation coefficient
- (4)
- Calculate the grey relational degree (GRD)
3. Selection of the Evaluation Indicators
3.1. Construction of the Evaluation Indicator System
3.2. Data Sources
4. Analysis Results
4.1. Calculation Results of the Indicator Weights
4.2. Calculation Results of the URT and SUD
4.3. Calculation Results of Coupling Coordination Degree Values
4.4. Calculation Results of Influencing Factors of Coupling Coordination Degree
5. Discussion
- (1)
- Cities with highly balanced development in two systems: Beijing, Shanghai and Guangzhou. They opened their first URT in 1971, 1993 and 1997, respectively, ranking high among the cities in mainland China that have opened URT. Due to the early start for metro development, strong industrial and economic strength and the URT system after years of construction and development, these first-tier cities have been scaled up and networked with a high degree of SUD [63]. The raw data show that the scale of rail transit passenger volume, line length and the number of operating vehicles is more significant in this category compared to other cities. The URT system formed a complete road network structure. At the same time, the overall urban sustainability value is also ranked high, indicating that this category of cities has a higher level of URT development, SUD and coupled and coordinated development between the two, which is worthy of reference for other cities.
- (2)
- Cities with barely balanced development in two systems: This includes eight cities, such as Shenzhen, Chengdu, Wuhan and Chongqing. From Figure 2, it can be seen that although the coupling and coordination between URT development and SUD in Chongqing are high, the gap between the comprehensive evaluation value of the two is significant. It indicates that the URT development in Chongqing still needs to be improved. It should scientifically plan and reasonably design the URT system, further develop the rail transit network structure and improve the rail transit operation mode and service quality, so that the level of coupling and coordination between URT development and SUD can be improved. Some scholars have related findings and recommendations [64]. However, the passenger volume in Suzhou and Tianjin is not supported enough. The original data show that the average daily passenger volume of URT in Suzhou and Tianjin in 2020 is only 84.5 and 92.6 10,000 persons, which is low compared to other cities. Some scholars found that URT is not the most preferred mode of transportation for Tianjin residents due to high fares, general walkable neighborhoods and inconvenient old subway stations [65]. For Suzhou and Tianjin, the attractiveness of rail transit to passengers can be increased by adjusting URT fares and other means. In addition, non-green transportation, such as private cars or cabs, can be appropriately restricted, thus, promoting green transportation development. Other cities in this category, such as Shenzhen, Chengdu and Nanjing, have a relatively good scale of URT development, which is compatible with the city’s sustainable development and positively impacts the city’s sustainable development.
- (3)
- Cities with slightly unbalanced development in two systems: This includes 25 cities, including Changsha, Shenyang and Qingdao; the total number of cities in this category accounts for more than 50% of the total cities studied. These cities are at a low level of coordinated development on a national scale. They need to improve their lagging items to improve the coupling and coordination between URT and SUD at a higher level. Most cities are slightly unbalanced with lagging uA type, indicating that the current process of rail transit construction in most Chinese cities is still slow and unable to provide public solid transportation support for rapid socio-economic development [15]. For example, Dongguan and Jinan, two cities, are similar to Chongqing in category 2 and have a higher overall urban sustainability system rating value than their counterparts. This indicates that the level of URT development has not kept up with the development of the cities and there is still a lot of room and potential for development. Cities, such as Lanzhou and Shijiazhuang, have low SUD levels compared to their counterparts. They should develop a public transportation strategy compatible with urban social and environmental development and transportation construction, focus on improving the technical equipment and technical performance of the existing URT, as well as the operation mode and service quality, to further reduce exhaust emissions and noise pollution and improve the level of SUD.
- (4)
- Cities with seriously unbalanced development in two systems: Foshan, Huaian, Zhuhai and Sanya. Cities in this category are at a low level for urban rail development and sustainable urban development systems. Both have much room for improvement. As can be seen from Table 7, the resident population is the most crucial factor affecting the level of coordination between URT development and SUD coupling. Cities should formulate their development strategies according to the size of their population, that is, the public transport demand, for example, Foshan with a high resident population in this category. The managers should insist on developing urban public transportation with rail transit as the core, increase rail transit investment and policy preferences and cooperate with the introduction of corresponding local policies to improve the efficiency of local URT development to improve the level of coordination between URT and SUD coupling [20,66]. However, as China’s urbanization process has been accelerating in recent years, the original approval standards are increasingly not applicable to the current level of urban socio-economic development. In the future, with the continuous development of the economy and society, the approval system of URT construction planning also needs to be improved continuously to improve and enrich the corresponding approval standard and approval content to ensure the healthy and stable dynamic coordination between URT construction and SUD [11,67]. Huai’an, Zhuhai and Sanya ranked at the bottom among all cities regarding Gross Regional Product. That is, the economy of the cities cannot create a good economic environment for the development of URT. In the future, such cities should pay more attention to the development of the economy.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
City | Scale (A1) | Operation Status (A2) | ||||||
---|---|---|---|---|---|---|---|---|
A11 | A12 | A13 | A14 | A21 | A22 | A23 | A24 | |
Beijing | 799.1 | 382 | 62 | 1108 | 10,367 | 626.9 | 0.78 | 67,257.0 |
Shanghai | 834.2 | 432 | 65 | 1038 | 8337 | 779.0 | 1.07 | 64,317.1 |
Tianjin | 238.8 | 157 | 15 | 224 | 1817 | 92.6 | 0.39 | 13,669.0 |
Chongqing | 343.3 | 178 | 20 | 328 | 3427 | 229.4 | 0.67 | 23,469.9 |
Guangzhou | 531.6 | 277 | 35 | 558 | 7073 | 660.2 | 1.19 | 41,422.4 |
Shenzhen | 422.6 | 263 | 48 | 532 | 4677 | 479.4 | 1.13 | 34,584.8 |
Wuhan | 387.5 | 254 | 29 | 493 | 3751 | 207.8 | 0.56 | 18,687.5 |
Nanjing | 394.3 | 187 | 13 | 291 | 3140 | 218.0 | 0.58 | 21,207.0 |
Shenyang | 211.5 | 157 | 13 | 180 | 1700 | 90.9 | 0.43 | 8125.0 |
Changchun | 117.7 | 119 | 8 | 134 | 1425 | 43.3 | 0.43 | 3621.7 |
Dalian | 181.3 | 106 | 3 | 114 | 1162 | 34.1 | 0.22 | 5379.1 |
Chengdu | 652.0 | 327 | 46 | 672 | 5062 | 399.2 | 0.72 | 29,226.5 |
Xi’an | 239.0 | 154 | 13 | 321 | 3437 | 247.6 | 1.04 | 15,631.6 |
Harbin | 30.3 | 26 | 1 | 31 | 485 | 14.0 | 0.46 | 1603.0 |
Suzhou | 210.1 | 151 | 9 | 221 | 2198 | 84.5 | 0.40 | 12,100.5 |
Zhengzhou | 244.0 | 133 | 17 | 175 | 1547 | 111.9 | 0.56 | 8217.6 |
Kunming | 139.4 | 83 | 9 | 122 | 1166 | 51.2 | 0.37 | 5523.4 |
Hangzhou | 300.6 | 169 | 20 | 325 | 2862 | 179.4 | 0.60 | 15,506.3 |
Foshan | 28.1 | 25 | 0 | 5 | 75 | 0.1 | 0.01 | 37.6 |
Changsha | 157.9 | 95 | 12 | 152 | 1736 | 122.0 | 0.77 | 8305.7 |
Ningbo | 154.3 | 97 | 6 | 150 | 1523 | 59.9 | 0.39 | 6183.1 |
Wuxi | 87.1 | 66 | 3 | 75 | 780 | 29.7 | 0.34 | 3318.5 |
Nanchang | 88.9 | 70 | 4 | 105 | 920 | 50.8 | 0.57 | 3883.7 |
Lanzhou | 86.9 | 26 | 0 | 26 | 270 | 14.3 | 0.55 | 1320.2 |
Qingdao | 255.0 | 119 | 4 | 191 | 1728 | 44.6 | 0.18 | 7340.3 |
Huaian | 20.1 | 23 | 0 | 26 | 203 | 1.9 | 0.10 | 565.4 |
Fuzhou | 58.5 | 45 | 1 | 59 | 482 | 25.9 | 0.44 | 2937.8 |
Dongguan | 37.8 | 15 | 0 | 20 | 259 | 9.6 | 0.25 | 2040.7 |
Nanning | 108.0 | 80 | 7 | 103 | 1181 | 61.2 | 0.57 | 5595.3 |
Hefei | 112.5 | 80 | 3 | 122 | 1255 | 56.1 | 0.50 | 6739.1 |
Shijiazhuang | 59.0 | 48 | 3 | 68 | 696 | 23.0 | 0.39 | 2394.5 |
Guiyang | 34.8 | 25 | 0 | 34 | 276 | 10.1 | 0.29 | 1888.4 |
Xiamen | 71.9 | 55 | 1 | 86 | 687 | 21.1 | 0.43 | 4691.1 |
Zhuhai | 8.8 | 14 | 0 | 12 | 93 | 0.3 | 0.03 | 24.2 |
Urumqi | 26.8 | 21 | 0 | 27 | 244 | 7.3 | 0.22 | 869.8 |
Wenzhou | 53.5 | 18 | 0 | 18 | 189 | 2.1 | 0.04 | 1219.3 |
Jinan | 47.7 | 24 | 0 | 42 | 486 | 2.4 | 0.05 | 2001.2 |
Changzhou | 34.2 | 29 | 0 | 28 | 228 | 6.2 | 0.18 | 1694.6 |
Xuzhou | 46.0 | 37 | 1 | 47 | 491 | 12.1 | 0.26 | 1126.4 |
Hohhot | 49.0 | 43 | 1 | 52 | 435 | 11.3 | 0.23 | 1348.9 |
Sanya | 8.4 | 15 | 0 | 11 | 157 | 0.3 | 0.03 | 11.0 |
Taiyuan | 23.6 | 22 | 0 | 16 | 258 | 14.8 | 0.62 | 22.8 |
City | Economic (B1) | |||||||
---|---|---|---|---|---|---|---|---|
B11 | B12 | B13 | B14 | B15 | B16 | B17 | B18 | |
Beijing | 164,889 | 827,343.8 | 3028 | 62,660.6 | 7,399,399 | 185,026 | 0.44 | 0.34 |
Shanghai | 155,800 | 584,114.4 | 8804 | 64,037.4 | 6,455,623 | 174,678 | 0.72 | 0.58 |
Tianjin | 101,614 | 238,194.9 | 5120 | 25,832.1 | 2,553,324 | 118,918 | 0.94 | 0.88 |
Chongqing | 78,173 | 128,607.7 | 6938 | 36,731.7 | 3,708,338 | 98,380 | 1.91 | 1.50 |
Guangzhou | 135,047 | 350,135.9 | 6208 | 49,192.4 | 4,193,638 | 135,138 | 0.82 | 0.65 |
Shenzhen | 159,309 | 539,404.1 | 11,255 | 49,148.2 | 5,052,706 | 139,436 | 0.53 | 0.44 |
Wuhan | 131,441 | 245,978.5 | 2958 | 49,877.1 | 1,763,564 | 107,567 | 2.15 | 1.83 |
Nanjing | 159,322 | 419,056.5 | 3231 | 77,285.8 | 2,161,081 | 138,005 | 1.42 | 1.30 |
Shenyang | 75,570 | 212,487.6 | 1592 | 40,106.1 | 1,184,237 | 95,908 | 1.52 | 1.42 |
Changchun | 77,634 | 156,071.7 | 1214 | 22,085.0 | 1,102,433 | 92,905 | 1.16 | 1.03 |
Dalian | 94,685 | 208,221.5 | 1898 | 24,536.9 | 1,047,641 | 98,812 | 0.96 | 0.84 |
Chengdu | 85,679 | 201,745.0 | 3664 | 38,752.0 | 11,433,200 | 104,463 | 1.76 | 1.35 |
Xi’an | 79,181 | 198,537.9 | 1667 | 38,497.9 | 2,135,688 | 104,363 | 1.98 | 1.61 |
Harbin | 54,570 | 137,356.2 | 1196 | 22,213.8 | 1,070,519 | 84,796 | 0.77 | 0.68 |
Suzhou | 158,466 | 275,809.3 | 11,900 | 60,407.7 | 2,974,094 | 113,744 | 1.72 | 1.56 |
Zhengzhou | 96,134 | 198,053.3 | 2295 | 40,224.3 | 2,139,900 | 89,464 | 2.71 | 2.40 |
Kunming | 80,584 | 192,962.8 | 997 | 36,293.6 | 1,126,367 | 102,304 | 2.22 | 1.80 |
Hangzhou | 136,617 | 433,525.8 | 5992 | 50,588.7 | 2,923,541 | 132,188 | 1.42 | 1.23 |
Foshan | 114,157 | 197,293.4 | 8020 | 34,549.3 | 1,513,137 | 94,536 | 2.27 | 1.78 |
Changsha | 123,297 | 228,695.5 | 2912 | 44,431.0 | 1,425,867 | 105,603 | 2.37 | 2.04 |
Ningbo | 132,614 | 245,930.8 | 8571 | 44,992.2 | 1,647,943 | 111,286 | 1.97 | 1.67 |
Wuxi | 165,851 | 252,918.4 | 7006 | 40,138.8 | 1,254,021 | 115,748 | 2.08 | 1.83 |
Nanchang | 92,697 | 216,082.5 | 1553 | 39,181.1 | 1,239,463 | 93,774 | 2.83 | 2.18 |
Lanzhou | 66,680 | 206,974.1 | 371 | 37,557.0 | 785,040 | 93,847 | 1.94 | 1.83 |
Qingdao | 123,828 | 196,061.3 | 3856 | 51,468.8 | 1,477,012 | 116,115 | 1.64 | 1.41 |
Huaian | 87,507 | 106,403.6 | 1486 | 36,751.2 | 449,447 | 83,216 | 2.03 | 1.85 |
Fuzhou | 121,015 | 208,930.1 | 2662 | 50,788.5 | 1,561,135 | 96,478 | 2.27 | 1.83 |
Dongguan | 92,176 | 166,416.8 | 11,525 | 35,688.4 | 2,863,056 | 79,601 | 0.84 | 0.73 |
Nanning | 54,669 | 131,408.6 | 1155 | 24,918.4 | 1,097,670 | 97,079 | 2.10 | 1.70 |
Hefei | 108,427 | 195,269.3 | 2150 | 48,172.5 | 1,729,908 | 104,818 | 1.59 | 1.38 |
Shijiazhuang | 52,961 | 146,692.9 | 2183 | 21,198.0 | 1,050,390 | 84,870 | 0.58 | 0.55 |
Guiyang | 72,246 | 208,433.6 | 764 | 36,531.9 | 1,132,677 | 101,829 | 2.07 | 1.85 |
Xiamen | 123,962 | 242,343.2 | 2420 | 44,283.2 | 1,267,026 | 108,554 | 1.20 | 0.73 |
Zhuhai | 145,645 | 382,989.0 | 1492 | 37,602.5 | 843,819 | 107,284 | 1.97 | 1.71 |
Urumqi | 82,314 | 237,147.1 | 445 | 25,765.7 | 819,995 | 98,907 | 1.80 | 1.60 |
Wenzhou | 71,766 | 156,746.2 | 6724 | 36,473.3 | 330,442 | 96,775 | 1.26 | 1.09 |
Jinan | 110,199 | 224,188.0 | 2215 | 48,367.2 | 1,549,779 | 108,391 | 1.45 | 1.24 |
Changzhou | 147,939 | 231,317.3 | 5065 | 45,859.1 | 671,577 | 113,273 | 1.97 | 1.66 |
Xuzhou | 80,673 | 100,682.7 | 2024 | 36,190.4 | 747,702 | 86,138 | 1.83 | 1.74 |
Hohhot | 81,656 | 177,279.5 | 252 | 29,940.1 | 437,896 | 89,549 | 1.20 | 1.11 |
Sanya | 68,656 | 156,830.0 | 33 | 36,639.2 | 151,084 | 93,152 | 0.74 | 0.63 |
Taiyuan | 78,734 | 267,151.7 | 622 | 31,111.1 | 1,013,630 | 88,650 | 1.48 | 1.32 |
City | Social (B2) | |||||||
---|---|---|---|---|---|---|---|---|
B21 | B22 | B23 | B24 | B25 | B26 | B27 | B28 | |
Beijing | 2189 | 7.67 | 10.94 | 1469.00 | 15,018,987 | 54.50 | 590,335 | 17,778,150 |
Shanghai | 2488 | 4.76 | 7.10 | 1237.85 | 4,737,785 | 57.73 | 540,693 | 16,166,700 |
Tianjin | 1387 | 14.91 | 8.94 | 1170.24 | 4,472,897 | 44.36 | 572,152 | 7,308,300 |
Chongqing | 3209 | 14.65 | 2.97 | 1565.61 | 9,735,406 | 54.52 | 915,556 | 12,033,548 |
Guangzhou | 1874 | 13.82 | 8.32 | 1350.40 | 3,571,356 | 49.66 | 1,307,144 | 8,204,077 |
Shenzhen | 1763 | 9.11 | 21.76 | 955.68 | 5,053,281 | 32.70 | 109,986 | 12,685,530 |
Wuhan | 1233 | 15.62 | 7.78 | 885.11 | 7,052,843 | 65.88 | 1,067,206 | 5,310,300 |
Nanjing | 932 | 25.00 | 9.39 | 868.28 | 5,940,046 | 61.65 | 918,141 | 3,376,045 |
Shenyang | 907 | 15.02 | 6.63 | 567.00 | 1,554,961 | 74.98 | 440,146 | 4,417,422 |
Changchun | 907 | 16.80 | 5.55 | 550.96 | 1,662,945 | 64.98 | 483,034 | 2,761,830 |
Dalian | 745 | 15.93 | 7.67 | 444.04 | 1,035,177 | 60.96 | 325,738 | 2,198,977 |
Chengdu | 2095 | 18.70 | 7.01 | 977.12 | 9,857,289 | 61.05 | 927,111 | 9,607,500 |
Xi’an | 1296 | 18.23 | 7.22 | 700.69 | 8,417,443 | 51.32 | 783,893 | 5,383,600 |
Harbin | 1001 | 16.01 | 7.21 | 473.00 | 2,030,700 | 76.82 | 591,940 | 2,754,830 |
Suzhou | 1275 | 26.92 | 4.98 | 481.33 | 3,094,452 | 49.89 | 263,246 | 5,978,357 |
Zhengzhou | 1262 | 9.61 | 5.00 | 640.80 | 4,958,199 | 72.69 | 1,160,303 | 5,682,800 |
Kunming | 846 | 12.58 | 7.79 | 482.80 | 1,635,613 | 68.90 | 697,961 | 2,058,113 |
Hangzhou | 1197 | 12.42 | 8.48 | 666.18 | 8,801,127 | 70.39 | 465,963 | 7,515,404 |
Foshan | 952 | 17.43 | 7.30 | 162.35 | 1,368,576 | 37.66 | 146,297 | 3,413,009 |
Changsha | 1006 | 22.29 | 11.79 | 409.51 | 4,016,368 | 66.81 | 697,407 | 4,160,672 |
Ningbo | 942 | 18.58 | 6.49 | 377.87 | 3,229,396 | 40.79 | 168,310 | 4,873,849 |
Wuxi | 746 | 27.15 | 4.07 | 349.55 | 1,483,873 | 57.48 | 133,163 | 4,141,800 |
Nanchang | 626 | 11.34 | 7.00 | 366.02 | 1,844,746 | 61.98 | 687,852 | 2,224,822 |
Lanzhou | 437 | 21.95 | 7.30 | 329.10 | 740,340.7 | 65.11 | 390,906 | 1,075,717 |
Qingdao | 1011 | 19.10 | 8.46 | 758.16 | 2,805,832 | 61.77 | 430,671 | 4,772,874 |
Huaian | 456 | 23.38 | 4.16 | 208.00 | 172,150.5 | 43.30 | 49,222 | 944,369 |
Fuzhou | 832 | 13.44 | 5.91 | 305.30 | 2,804,912 | 41.46 | 363,738 | 2,215,488 |
Dongguan | 1048 | 11.13 | 5.61 | 1194.31 | 1,131,446 | 31.39 | 134,546 | 5,809,506 |
Nanning | 875 | 20.44 | 4.19 | 326.70 | 2,035,307 | 48.77 | 568,756 | 1,880,246 |
Hefei | 937 | 18.76 | 6.68 | 502.50 | 2,967,805 | 63.26 | 586,170 | 2,877,855 |
Shijiazhuang | 1124 | 18.83 | 3.71 | 311.83 | 1,041,077 | 49.53 | 583,472 | 2,804,498 |
Guiyang | 599 | 16.73 | 4.90 | 369.00 | 1,614,016 | 61.23 | 440,212 | 2,542,439 |
Xiamen | 518 | 17.67 | 8.33 | 401.94 | 2,077,884 | 35.21 | 169,288 | 3,180,800 |
Zhuhai | 245 | 12.93 | 10.07 | 152.85 | 1,023,091 | 41.09 | 143,778 | 1,468,847 |
Urumqi | 405 | 19.68 | 11.01 | 521.60 | 618,179 | 74.98 | 237,556 | 1,596,259 |
Wenzhou | 959 | 16.72 | 2.84 | 275.87 | 1,455,280 | 40.33 | 120,734 | 3,403,512 |
Jinan | 924 | 19.67 | 8.63 | 793.65 | 3,349,288 | 63.63 | 687,878 | 4,373,163 |
Changzhou | 528 | 25.74 | 4.59 | 277.29 | 1,012,432 | 45.19 | 145,032 | 1,716,587 |
Xuzhou | 908 | 23.43 | 3.07 | 289.64 | 1,402,594 | 45.85 | 145,857 | 2,138,916 |
Hohhot | 345 | 14.30 | 8.29 | 272.16 | 1,062,088 | 54.26 | 248,552 | 924,255 |
Sanya | 104 | 17.48 | 10.74 | 51.63 | 194,063.6 | 45.20 | 60,798 | 364,160 |
Taiyuan | 532 | 17.70 | 7.00 | 340.00 | 1,283,156 | 78.61 | 482,167 | 1,741,964 |
City | Environmental (B3) | ||||||||
---|---|---|---|---|---|---|---|---|---|
B31 | B32 | B33 | B34 | B35 | B36 | B37 | B38 | B39 | |
Beijing | 38 | 4 | 29 | 276 | 154.19 | 42.34 | 847.02 | 96.56% | 16,775.08 |
Shanghai | 32 | 6 | 37 | 319 | 203.92 | 66.16 | 361.34 | 96.68% | 18,699.42 |
Tianjin | 48 | 8 | 39 | 245 | 115.69 | 31.51 | 433.72 | 96.42% | 13,287.01 |
Chongqing | 53 | 8 | 39 | 135 | 179.80 | 22.03 | 163.97 | 98.17% | 22,754.62 |
Guangzhou | 23 | 7 | 36 | 331 | 316.25 | 78.87 | 144.14 | 97.90% | 24,458.15 |
Shenzhen | 19 | 6 | 23 | 355 | 230.02 | 55.12 | 193.52 | 98.11% | 26,706.00 |
Wuhan | 37 | 8 | 36 | 309 | 234.23 | 26.60 | 202.31 | 97.00% | 21,171.79 |
Nanjing | 31 | 7 | 36 | 304 | 296.54 | 100.33 | 150.95 | 97.90% | 8628.71 |
Shenyang | 42 | 18 | 35 | 287 | 196.68 | 26.50 | 73.20 | 98.94% | 15,741.33 |
Changchun | 42 | 10 | 32 | 305 | 150.13 | 48.82 | 95.50 | 95.69% | 6975.00 |
Dalian | 30 | 10 | 25 | 332 | 153.63 | 51.41 | 68.88 | 98.78% | 7309.40 |
Chengdu | 41 | 6 | 37 | 280 | 280.44 | 17.34 | 165.75 | 97.62% | 16,623.68 |
Xi’an | 51 | 8 | 41 | 250 | 177.70 | 27.34 | 249.02 | 96.66% | 12,268.13 |
Harbin | 47 | 17 | 32 | 303 | 141.07 | 15.34 | 71.27 | 95.23% | 9742.00 |
Suzhou | 31 | 8 | 34 | 307 | 261.04 | 18.51 | 101.62 | 96.84% | 11,332.25 |
Zhengzhou | 51 | 9 | 39 | 230 | 128.95 | 20.34 | 122.05 | 98.51% | 7578.32 |
Kunming | 24 | 9 | 25 | 366 | 157.99 | 22.33 | 38.71 | 98.89% | 8059.64 |
Hangzhou | 30 | 6 | 38 | 334 | 244.82 | 41.41 | 161.16 | 97.11% | 11,171.93 |
Foshan | 22 | 7 | 31 | 333 | 335.81 | 7.68 | 115.16 | 100.34% | 4381.10 |
Changsha | 41 | 6 | 28 | 309 | 277.33 | 14.39 | 79.11 | 98.40% | 5187.20 |
Ningbo | 23 | 8 | 32 | 340 | 250.14 | 17.52 | 120.06 | 99.73% | 5153.57 |
Wuxi | 33 | 7 | 35 | 299 | 196.22 | 26.66 | 150.28 | 98.92% | 4645.00 |
Nanchang | 33 | 9 | 29 | 335 | 219.78 | 23.66 | 78.49 | 98.84% | 5242.04 |
Lanzhou | 34 | 15 | 47 | 312 | 174.12 | 22.24 | 396.43 | 96.35% | 5065.99 |
Qingdao | 31 | 7 | 31 | 315 | 142.91 | 41.37 | 129.32 | 98.20% | 7489.70 |
Huaian | 42 | 7 | 25 | 294 | 155.62 | 20.50 | 62.98 | 95.76% | 3396.38 |
Fuzhou | 21 | 5 | 21 | 364 | 224.16 | 15.63 | 34.39 | 96.88% | 4535.36 |
Dongguan | 24 | 8 | 27 | 334 | 168.75 | 70.65 | 115.56 | 96.21% | 20,855.68 |
Nanning | 31 | 8 | 24 | 357 | 314.12 | 16.25 | 35.52 | 100.00% | 8175.72 |
Hefei | 36 | 7 | 39 | 310 | 243.82 | 21.63 | 122.49 | 97.75% | 8149.00 |
Shijiazhuang | 58 | 12 | 41 | 205 | 122.22 | 13.10 | 128.35 | 99.30% | 7551.20 |
Guiyang | 41 | 10 | 18 | 362 | 216.50 | 33.21 | 72.13 | 98.09% | 5709.00 |
Xiamen | 18 | 6 | 19 | 365 | 197.95 | 45.69 | 63.11 | 100.00% | 4965.46 |
Zhuhai | 19 | 5 | 24 | 342 | 292.69 | 127.58 | 69.24 | 96.81% | 6256.92 |
Urumqi | 47 | 9 | 42 | 279 | 134.92 | 81.90 | 700.23 | 99.20% | 5034.00 |
Wenzhou | 23 | 6 | 30 | 355 | 235.00 | 10.05 | 32.56 | 98.12% | 3716.18 |
Jinan | 47 | 12 | 35 | 227 | 126.00 | 31.61 | 164.84 | 99.23% | 8934.00 |
Changzhou | 39 | 9 | 35 | 295 | 234.95 | 23.81 | 287.03 | 98.06% | 3528.00 |
Xuzhou | 50 | 10 | 35 | 261 | 167.87 | 19.12 | 73.53 | 94.95% | 3586.98 |
Hohhot | 40 | 18 | 39 | 294 | 89.24 | 45.46 | 174.01 | 98.94% | 5189.82 |
Sanya | 11 | 4 | 9 | 365 | 399.16 | 20.90 | 82.91 | 96.44% | 1825.00 |
Taiyuan | 54 | 23 | 48 | 224 | 150.50 | 26.32 | 174.89 | 103.24% | 6568.80 |
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Value of D | Comprehensive Type | Comparison of u | Subtype |
---|---|---|---|
0.75 ≤ D ≤ 1 | Highly balanced | uA < uB | Highly balanced with lagging uA |
uA ≈ uB | Highly balanced | ||
uA > uB | Highly balanced with lagging uB | ||
0.5 ≤ D < 0.75 | Barely balanced | uA < uB | Barely balanced with lagging uA |
uA ≈ uB | Barely balanced | ||
uA > uB | Barely balanced with lagging uB | ||
0.25 ≤ D < 0.5 | Slightly unbalanced | uA < uB | Slightly unbalanced with lagging uA |
uA ≈ uB | Slightly unbalanced | ||
uA > uB | Slightly unbalanced with lagging uB | ||
0 ≤ D < 0.25 | Seriously unbalanced | uA < uB | Seriously unbalanced with lagging uA |
uA ≈ uB | Seriously unbalanced | ||
uA > uB | Seriously unbalanced with lagging uB |
System Layer | Factor Layer | Indicator Layer | Indicator Direction (+/−) | Unit | Supporting Literature References |
---|---|---|---|---|---|
URT development system (A) | Scale (A1) | Length of Lines (A11) | + | km | [20] |
Number of Stations (A12) | + | unit | [20,26] | ||
Number of Transfer Stations (A13) | + | unit | [55] | ||
Number of Vehicles in Service (A14) | + | unit | [20] | ||
Operation status (A2) | Daily Average Times of the Train Operations (A21) | + | unit | [55] | |
Average Daily Passenger Volume (A22) | + | 10,000 persons | [26,49,56] | ||
Passenger Transport Intensity (A23) | + | 10,000 person/km day | [20,49,56] | ||
Operation Mileage (A24) | + | 10,000 vehicle km | [26] | ||
Sustainable urban development system (B) | Economic (B1) | Per Capita Gross Regional Product (B11) | + | yuan | [57] |
Per Capita Deposits of Financial Institutions at Year-end (B12) | + | yuan | [45] | ||
Number of Industrial Enterprises (B13) | + | unit | [6] | ||
Per Capita Retail Sales of Consumer Goods (B14) | + | yuan | [26,27] | ||
Persons Employed in Urban Non-Private Units at Year-end (B15) | + | 10,000 person | [27] | ||
Average Wage of Employed Staff and Workers in Urban Non-Private Units (B16) | + | yuan | [57] | ||
Per Sales Area of Commercial Residential Building (B17) | + | 10,000 sq.m | [27,58] | ||
Per Sales Area of Residential Buildings (B18) | + | 10,000 sq.m | [27,58] | ||
Social (B2) | Resident Population (B21) | + | 10,000 person | [41] | |
Per Capita Road Area (B22) | + | sq.m | [41] | ||
Buses under Operation (B23) | + | unit | [6,59] | ||
Area of Built District (B24) | + | sq.km | [60] | ||
Fixed Assets Investment in Urban Service Facilities (B25) | + | 10,000 yuan | [27,58] | ||
Per Capita Number of Beds of Hospitals (B26) | + | unit | [59,61] | ||
Undergraduate in Regular HEIs (B27) | + | 10,000 person | [39] | ||
Number of Employees Joining Urban Basic Pension Insurance (B28) | + | 10,000 person | [54] | ||
Environmental (B3) | Annual Mean Concentration of PM2.5 (B31) | − | ug/m3 | [6,7] | |
Annual Mean Concentration of SO2 (B32) | − | ug/m3 | [6,7,59] | ||
Annual Mean Concentration of NO2 (B33) | − | ug/m3 | [6,7,59] | ||
Days with good air quality (B34) | + | unit | [7] | ||
Daily Water Consumption Per Capita (B35) | − | litre | [7,57,62] | ||
Per Capita Area of Parks and Green Space (B36) | + | 10,000 sq.m | [6,57,60,59] | ||
Per Capita Natural Gas Supplied (B37) | − | 10,000 cu.m | [6,7,59,62] | ||
Wastewater Treatment Rate (B38) | + | % | [59] | ||
Surface Area of Roads Cleaned and Maintained (B39) | + | 10,000 sq.m | [7] |
System Layer | Factor Layer | Indicator Layer | ||
---|---|---|---|---|
Code | Weight | Code | Weight | |
URT development system (A) | A1 | 0.5102 | A11 | 0.1064 |
A12 | 0.1037 | |||
A13 | 0.1725 | |||
A14 | 0.1276 | |||
A2 | 0.4898 | A21 | 0.1239 | |
A22 | 0.1718 | |||
A23 | 0.0471 | |||
A24 | 0.1470 | |||
Sustainable urban development system (B) | B1 | 0.3946 | B11 | 0.0416 |
B12 | 0.0610 | |||
B13 | 0.0665 | |||
B14 | 0.0379 | |||
B15 | 0.0779 | |||
B16 | 0.0503 | |||
B17 | 0.0310 | |||
B18 | 0.0283 | |||
B2 | 0.3624 | B21 | 0.0361 | |
B22 | 0.0180 | |||
B23 | 0.0434 | |||
B24 | 0.0423 | |||
B25 | 0.0758 | |||
B26 | 0.0298 | |||
B27 | 0.0532 | |||
B28 | 0.0638 | |||
B3 | 0.2430 | B31 | 0.0268 | |
B32 | 0.0103 | |||
B33 | 0.0253 | |||
B34 | 0.0105 | |||
B35 | 0.0143 | |||
B36 | 0.0641 | |||
B37 | 0.0083 | |||
B38 | 0.0278 | |||
B39 | 0.0556 |
City | Relative Closeness Value (uA) | Rank | City | Relative Closeness Value (uA) | Rank |
---|---|---|---|---|---|
Shanghai | 0.9297 | 1 | Hefei | 0.1082 | 22 |
Beijing | 0.8952 | 2 | Nanchang | 0.0975 | 23 |
Guangzhou | 0.6413 | 3 | Wuxi | 0.0727 | 24 |
Chengdu | 0.5874 | 4 | Xiamen | 0.0716 | 25 |
Shenzhen | 0.5812 | 5 | Lanzhou | 0.0640 | 26 |
Wuhan | 0.3864 | 6 | Taiyuan | 0.0640 | 27 |
Chongqing | 0.3321 | 7 | Shijiazhuang | 0.0637 | 28 |
Nanjing | 0.3029 | 8 | Fuzhou | 0.0616 | 29 |
Xi’an | 0.2915 | 9 | Harbin | 0.0528 | 30 |
Hangzhou | 0.2875 | 10 | Hohhot | 0.0403 | 31 |
Tianjin | 0.2128 | 11 | Xuzhou | 0.0401 | 32 |
Zhengzhou | 0.2050 | 12 | Guiyang | 0.0360 | 33 |
Suzhou | 0.1880 | 13 | Dongguan | 0.0313 | 34 |
Shenyang | 0.1869 | 14 | Jinan | 0.0264 | 35 |
Changsha | 0.1777 | 15 | Changzhou | 0.0264 | 36 |
Qingdao | 0.1459 | 16 | Urumqi | 0.0261 | 37 |
Changchun | 0.1259 | 17 | Wenzhou | 0.0185 | 38 |
Ningbo | 0.1256 | 18 | Huaian | 0.0148 | 39 |
Kunming | 0.1194 | 19 | Foshan | 0.0100 | 40 |
Nanning | 0.1167 | 20 | Sanya | 0.0040 | 41 |
Dalian | 0.1086 | 21 | Zhuhai | 0.0031 | 42 |
Mean | 0.1876 |
City | Relative Closeness Value (uB) | Rank | City | Relative Closeness Value (uB) | Rank |
---|---|---|---|---|---|
Beijing | 0.6106 | 1 | Nanchang | 0.2749 | 22 |
Shanghai | 0.5687 | 2 | Kunming | 0.2749 | 23 |
Shenzhen | 0.5416 | 3 | Shenyang | 0.2716 | 24 |
Guangzhou | 0.5000 | 4 | Hefei | 0.2711 | 25 |
Chengdu | 0.4907 | 5 | Urumqi | 0.2706 | 26 |
Chongqing | 0.4658 | 6 | Xiamen | 0.2693 | 27 |
Nanjing | 0.4495 | 7 | Fuzhou | 0.2691 | 28 |
Hangzhou | 0.4318 | 8 | Changzhou | 0.2688 | 29 |
Wuhan | 0.4074 | 9 | Guiyang | 0.2413 | 30 |
Suzhou | 0.4027 | 10 | Taiyuan | 0.2399 | 31 |
Dongguan | 0.3858 | 11 | Dalian | 0.2390 | 32 |
Zhengzhou | 0.3463 | 12 | Nanning | 0.2325 | 33 |
Zhuhai | 0.3459 | 13 | Wenzhou | 0.2318 | 34 |
Tianjin | 0.3408 | 14 | Changchun | 0.2250 | 35 |
Xi’an | 0.3338 | 15 | Harbin | 0.2185 | 36 |
Ningbo | 0.3303 | 16 | Lanzhou | 0.2113 | 37 |
Wuxi | 0.3222 | 17 | Sanya | 0.1932 | 38 |
Changsha | 0.3197 | 18 | Hohhot | 0.1906 | 39 |
Qingdao | 0.3178 | 19 | Huaian | 0.1889 | 40 |
Jinan | 0.3034 | 20 | Xuzhou | 0.1846 | 41 |
Foshan | 0.2901 | 21 | Shijiazhuang | 0.1843 | 42 |
Mean | 0.3204 |
City | C | T | D | D Rank | City | C | T | D | D Rank |
---|---|---|---|---|---|---|---|---|---|
Beijing | 0.9820 | 0.7529 | 0.8598 | 1 | Nanchang | 0.8792 | 0.1862 | 0.4046 | 22 |
Shanghai | 0.9705 | 0.7492 | 0.8527 | 2 | Dalian | 0.9269 | 0.1738 | 0.4013 | 23 |
Guangzhou | 0.9923 | 0.5707 | 0.7525 | 3 | Wuxi | 0.7752 | 0.1974 | 0.3912 | 24 |
Shenzhen | 0.9994 | 0.5614 | 0.7490 | 4 | Xiamen | 0.8146 | 0.1704 | 0.3726 | 25 |
Chengdu | 0.9960 | 0.5391 | 0.7327 | 5 | Fuzhou | 0.7786 | 0.1654 | 0.3588 | 26 |
Wuhan | 0.9997 | 0.3969 | 0.6299 | 6 | Taiyuan | 0.8155 | 0.1519 | 0.3520 | 27 |
Chongqing | 0.9859 | 0.3990 | 0.6271 | 7 | Lanzhou | 0.8450 | 0.1376 | 0.3410 | 28 |
Nanjing | 0.9809 | 0.3762 | 0.6074 | 8 | Dongguan | 0.5272 | 0.2086 | 0.3316 | 29 |
Hangzhou | 0.9797 | 0.3597 | 0.5936 | 9 | Shijiazhuang | 0.8737 | 0.1240 | 0.3291 | 30 |
Xi’an | 0.9977 | 0.3127 | 0.5585 | 10 | Harbin | 0.7919 | 0.1356 | 0.3277 | 31 |
Suzhou | 0.9316 | 0.2954 | 0.5246 | 11 | Guiyang | 0.6721 | 0.1387 | 0.3053 | 32 |
Tianjin | 0.9729 | 0.2768 | 0.5189 | 12 | Jinan | 0.5425 | 0.1649 | 0.2991 | 33 |
Zhengzhou | 0.9666 | 0.2756 | 0.5162 | 13 | Hohhot | 0.7591 | 0.1154 | 0.2960 | 34 |
Changsha | 0.9584 | 0.2487 | 0.4882 | 14 | Xuzhou | 0.7656 | 0.1123 | 0.2932 | 35 |
Shenyang | 0.9828 | 0.2293 | 0.4747 | 15 | Changzhou | 0.5704 | 0.1476 | 0.2901 | 36 |
Qingdao | 0.9287 | 0.2318 | 0.4640 | 16 | Urumqi | 0.5667 | 0.1484 | 0.2900 | 37 |
Ningbo | 0.8936 | 0.2279 | 0.4513 | 17 | Wenzhou | 0.5234 | 0.1251 | 0.2559 | 38 |
Kunming | 0.9189 | 0.1972 | 0.4256 | 18 | Foshan | 0.3593 | 0.1500 | 0.2322 | 39 |
Hefei | 0.9030 | 0.1896 | 0.4138 | 19 | Huaian | 0.5184 | 0.1018 | 0.2298 | 40 |
Changchun | 0.9593 | 0.1755 | 0.4103 | 20 | Zhuhai | 0.1886 | 0.1745 | 0.1814 | 41 |
Nanning | 0.9434 | 0.1746 | 0.4059 | 21 | Sanya | 0.2807 | 0.0986 | 0.1664 | 42 |
Mean | 0.8099 | 0.2540 | 0.4406 |
Indicators | GRD | Rank | Indicators | GRD | Rank |
---|---|---|---|---|---|
B21 | 0.9628 | 1 | A22 | 0.9077 | 18 |
B16 | 0.9608 | 2 | A13 | 0.9074 | 19 |
B24 | 0.9542 | 3 | A24 | 0.9051 | 20 |
B39 | 0.9485 | 4 | B36 | 0.9022 | 21 |
B11 | 0.9410 | 5 | B27 | 0.8977 | 22 |
B14 | 0.9408 | 6 | B31 | 0.8859 | 23 |
A23 | 0.9390 | 7 | B38 | 0.8719 | 24 |
B12 | 0.9379 | 8 | B26 | 0.8701 | 25 |
B23 | 0.9367 | 9 | B33 | 0.8512 | 26 |
A12 | 0.9328 | 10 | B34 | 0.8489 | 27 |
B28 | 0.9310 | 11 | B13 | 0.8399 | 28 |
B15 | 0.9249 | 12 | B35 | 0.8084 | 29 |
A11 | 0.9207 | 13 | B32 | 0.6834 | 30 |
B25 | 0.9189 | 14 | B22 | 0.6723 | 31 |
A14 | 0.9097 | 15 | B17 | 0.5509 | 32 |
A21 | 0.9097 | 16 | B18 | 0.5173 | 33 |
B37 | 0.9091 | 17 |
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Jiao, L.; Wu, F.; Zhu, Y.; Luo, Q.; Luo, F.; Zhang, Y. Research on the Coupling Coordination Relationship between Urban Rail Transit System and Sustainable Urban Development. Systems 2022, 10, 110. https://doi.org/10.3390/systems10040110
Jiao L, Wu F, Zhu Y, Luo Q, Luo F, Zhang Y. Research on the Coupling Coordination Relationship between Urban Rail Transit System and Sustainable Urban Development. Systems. 2022; 10(4):110. https://doi.org/10.3390/systems10040110
Chicago/Turabian StyleJiao, Liudan, Fengyan Wu, Yinghan Zhu, Qiudie Luo, Fenglian Luo, and Yu Zhang. 2022. "Research on the Coupling Coordination Relationship between Urban Rail Transit System and Sustainable Urban Development" Systems 10, no. 4: 110. https://doi.org/10.3390/systems10040110