枢纽机场的国际中转客流拥堵溢出效应研究

doi:10.11821/dlyj020190151

地理研究 ›› 2019, Vol. 38 ›› Issue (11): 2716-2729.doi: 10.11821/dlyj020190151

枢纽机场的国际中转客流拥堵溢出效应研究

张生润¹, 郑海龙¹, 李涛², 唐小卫¹, 王姣娥^3,⁴

^1. 南京航空航天大学民航学院,南京 211106
^2. 陕西师范大学西北国土资源研究中心,西安 710119
^3. 中国科学院地理科学与资源研究所区域可持续发展分析与模拟重点实验室,北京 100101
^4. 中国科学院大学资源与环境学院,北京 100049

收稿日期:2019-02-27 修回日期:2019-07-16 出版日期:2019-11-20 发布日期:2019-12-02
作者简介:张生润（1985-）,女,甘肃白银人,博士,讲师,硕士生导师,研究方向为航空运输地理,运输经济。E-mail: zhangshengrun@nuaa.edu.cn
基金资助:
国家自然科学基金项目(41701120);国家自然科学基金项目(41501120);国家自然科学基金项目(61603178)

Research on congestion spillover effects of international transfer traffic on hub airports

ZHANG Shengrun¹, ZHENG Hailong¹, LI Tao², TANG Xiaowei¹, WANG Jiaoe^3,⁴

^1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
^2. Center for Land Resource Research in Northwest China, Shaanxi Normal University, Xi’an 710119, China;
^3. Key Laboratory of Regional Sustainable Development Analysis and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
^4. College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-02-27 Revised:2019-07-16 Online:2019-11-20 Published:2019-12-02

摘要/Abstract

摘要：

提高国际枢纽机场中转能力是新时代民航强国战略下拓展国际航空市场的基础保障。基于2010—2017年OAG全球旅客流量流向数据,本文采用两阶段面板数据模型,以北京首都、上海浦东和广州白云机场为案例,分析了容量受限导致拥堵和枢纽机场竞争全球化背景下中国三大机场的国际中转客流拥堵溢出效应。研究表明三大机场的溢出效应主要由与其国际市场重叠率较高的国外枢纽机场承接,国内二级机场承接率相对有限。表现为：北京首都溢出旅客量流向厦门、曼谷和迪拜;上海浦东流向吉隆坡、首尔和乌鲁木齐;广州白云流向香港、武汉、西安、首尔、吉隆坡、伊斯坦布尔、新加坡和赫尔辛基。结果讨论了应加强国内二级机场的航线与航班设计,以分别承接三大机场的溢出效应。

关键词: 拥堵溢出效应, 国际中转客流, 机场竞争, 中国三大枢纽机场, 面板数据模型

Abstract:

Based upon the strategy of developing civil aviation to drive the prosperity of China, it is fundamental and significant to enhance the transfer level of international hub airports in China. This is particularly urgent during the process of enlarging the international air transport market. As hub competition becomes a worldwide phenomenon, attracting more transfer passengers has been challenging for both hub airports and their dominant full-service carriers when we design a complex hub-and-spoke network configuration. The capacity constraints at big hub airports, however, lead to severe congestion, which limits their accommodation of the increasing number of transfer passengers. In this way, there is a larger probability that transfers passengers to other hub airports located in other regions, i.e., the so-called “congestion spillover effects”. This paper examines the congestion spillover effects of the three biggest Chinese hub airports (i.e., Beijing Capital International Airport, Shanghai Pudong International Airport and Guangzhou Baiyun International Airport) by exploring a two-stage panel data modeling framework. Using OAG traffic analyser data between 2010 and 2017, the models are estimated by fixed-effects and systems of regression panel data methods. The results show that the spillovers of international transfer traffic at the “Big Three (B3)” have been mainly taken by the hub airports located outside China that have larger overlap rates with the B3. The secondary hub airports in China show limited capability to capture the spillovers of the B3. The spillover transfer traffic of the B3 spreads to different branches of geography. In specific: from Beijing Capital to Xiamen, Bangkok, and Dubai; from Shanghai Pudong to Kuala Lumpur, Seoul and Urumqi; from Guangzhou Baiyun to Hong Kong, Wuhan, Xi’an, Seoul, Kuala Lumpur, Istanbul, Singapore and Helsinki. This paper further discusses the necessity of China to develop its secondary hub airports to overtake the spillovers of the B3. If the overlap rates between the primary and secondary hub airports are large, the latter can play a role as complementary airports. Otherwise, the secondary hub airports can develop towards specialization to cover the regions that cannot be served by their primary counterparts. In the case of the spillovers of the B3 being captured by hubs located in other countries, the dominant carriers at the B3 can consider to establish strategic alliance cooperation with their dominant carriers.

Key words: congestion spillover effects, international transfer flow, airport competition, three Chinese hub airports, panel data model

张生润, 郑海龙, 李涛, 唐小卫, 王姣娥. 枢纽机场的国际中转客流拥堵溢出效应研究[J]. 地理研究, 2019, 38(11): 2716-2729.

ZHANG Shengrun, ZHENG Hailong, LI Tao, TANG Xiaowei, WANG Jiaoe. Research on congestion spillover effects of international transfer traffic on hub airports[J]. GEOGRAPHICAL RESEARCH, 2019, 38(11): 2716-2729.

图/表 6

图1

图2

表1

表2

中国机场两阶段面板数据模型估计结果"

		$β 0$	$β 1$	$β 2$	北京首都溢出	上海浦东溢出	广州白云溢出	N	R²(%)
中国 (14)	香港 (HKG)	0.770*** (0.030)	0.068*** (0.015)	0.006*** (0.001)	-0.196 (0.172)	-0.128** (0.042)	-0.839*** (0.148)	49	89.6
	昆明 (KMG)	1.130*** (0.119)	0.055** (0.025)	-0.003 (0.002)	-0.298* (0.176)	-0.001 (0.028)	0.157 (0.173)	34	93.6
	厦门 (XMN)	1.041*** (0.068)	0.020* (0.011)	0.005*** (0.002)	-0.206** (0.100)	-0.037 (0.029)	-0.065 (0.099)	30	94.2
	深圳 (SZX)	1.596*** (0.209)	-0.012 (0.016)	0.002 (0.001)	0.012 (0.108)	0.013 (0.017)	-0.080 (0.118)	25	90.0
	成都 (CTU)	0.943*** (0.092)	0.008 (0.005)	0.001* (0.001)	-0.000 (0.028)	-0.003 (0.012)	0.030 (0.033)	43	86.4
	乌鲁木齐 (URC)	0.549*** (0.189)	-0.006 (0.011)	0.002 (0.001)	-0.019 (0.068)	-0.066*** (0.024)	0.171 (0.123)	33	59.9
	青岛 (TAO)	0.677 (0.450)	-0.006 (0.006)	0.001 (0.001)	0.013 (0.069)	0.080 (0.047)	0.001 (0.020)	29	45.3
	重庆 (CKG)	0.018 (0.069)	0.010 (0.008)	0.003** (0.001)	0.014 (0.084)	-0.027* (0.014)	0.101*** (0.036)	30	87.6
	武汉 (WUH)	0.762*** (0.085)	0.002 (0.004)	0.001*** (0.000)	-0.010 (0.024)	-0.006 (0.006)	-0.068*** (0.014)	36	87.0
	福州 (FOC)	1.039*** (0.231)	-0.009 (0.012)	0.002* (0.001)	-0.097 (0.084)	-0.004 (0.032)	-0.004 (0.046)	22	83.8
	上海虹桥 (SEA)	0.226 (0.323)	-0.015 (0.061)	0.000 (0.003)	0.012 (0.152)	-0.036 (0.036)	0.147 (0.381)	14	19.4
	西安 (XIY)	0.349 (0.213)	-0.010*** (0.003)	0.002*** (0.001)	0.035 (0.047)	0.015 (0.014)	-0.055*** (0.018)	31	81.3
	南京 (NKG)	0.675*** (0.157)	0.007** (0.003)	0.001** (0.000)	-0.006 (0.020)	-0.006 (0.015)	0.031 (0.018)	30	87.6
	杭州 (HGH)	0.921*** (0.104)	0.000 (0.002)	0.001** (0.000)	-0.016 (0.019)	0.003 (0.005)	-0.026 (0.019)	34	87.1

表2

表3

世界其他区域机场两阶段面板数据模型估计结果"

		$β 0$	$β 1$	$β 2$	北京首都溢出	上海浦东溢出	广州白云溢出	N	R²(%)
亚洲 (4)	首尔仁川 (ICN)	0.536*** (0.091)	0.051*** (0.013)	-0.003** (0.001)	-0.026 (0.059)	-0.341*** (0.054)	-0.237*** (0.031)	56	59.8
	曼谷 (BKK)	1.002*** (0.048)	0.034*** (0.011)	-0.002* (0.001)	-0.130** (0.063)	0.007 (0.021)	-0.072 (0.081)	42	95.6
	新加坡 (SIN)	1.008*** (0.040)	0.021*** (0.008)	-0.001* (0.001)	-0.050 (0.043)	-0.003 (0.012)	-0.176** (0.078)	41	97.9
	吉隆坡 (KUL)	0.153 (0.169)	0.017 (0.020)	-0.005 (0.010)	-0.128 (0.153)	-0.546** (0.249)	-0.399*** (0.109)	21	72.5
欧洲和北美 (8)	法兰克福 (FRA)	0.904*** (0.047)	0.195*** (0.050)	-0.143** (0.058)	-0.047 (0.145)	0.072 (0.096)	-0.679 (0.442)	21	98.5
	阿姆斯特丹 (AMS)	0.106 (0.160)	0.092* (0.024)	-0.041 (0.035)	0.143 (0.073)	0.003 (0.074)	-0.302 (0.271)	21	61.8
	莫斯科 (SVO)	1.056*** (0.068)	0.047* (0.026)	-0.009 (0.062)	-0.126 (0.214)	0.002 (0.048)	0.105 (0.553)	21	78.5
	巴黎戴高乐 (CDG)	0.956*** (0.035)	0.093*** (0.024)	-0.052* (0.029)	0.096 (0.067)	0.040 (0.048)	-0.400* (0.231)	21	99.4
	赫尔辛基 (HEL)	0.912*** (0.047)	0.101*** (0.021)	-0.087*** (0.024)	-0.031 (0.064)	0.089** (0.040)	-0.400** (0.182)	21	98.0
	伊斯坦布尔 (IST)	0.852*** (0.037)	0.141*** (0.013)	-0.103*** (0.015)	0.029 (0.043)	0.018* (0.010)	-0.614*** (0.098)	25	96.7
	芝加哥奥黑尔 (ORD)	0.852*** (0.082)	-0.092 (0.066)	0.171*** (0.060)	-0.113 (0.158)	-0.557 (0.223)	1.751 (0.563)	9	98.0
	旧金山 (SFO)	0.422 (0.001)	1.153 (0.001)	0.435 (0.001)	-14.743 (0.001)	14.586 (0.001)	43.525 (0.001)	7	98.0
中东 (2)	迪拜 (DXB)	1.024*** (0.054)	0.028 (0.032)	0.003 (0.037)	-0.191** (0.092)	-0.028 (0.061)	0.075 (0.215)	28	98.2
中东 (2)	多哈 (DOH)	1.059*** (0.062)	0.041** (0.019)	-0.015 (0.022)	0.080 (0.059)	0.016 (0.033)	-0.088 (0.129)	28	98.3

表3

表4

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区域与国家（个数）	枢纽机场		航线重叠率 (%)
区域与国家（个数）	枢纽机场	北京首都	上海浦东	广州白云
中国 (14)	香港 (HKG)	24.7	36.2	60.6
	昆明 (KMG)	6.7	5.1	44.6
	厦门 (XMN)	44.0	46.6	63.2
	深圳 (SZX)	60.0	71.1	86.5
	成都 (CTU)	41.7	38.6	39.2
	乌鲁木齐 (URC)	10.8	12.8	15.5
	青岛 (TAO)	3.8	11.2	15.3
	重庆 (CKG)	8.8	12.6	8.4
	武汉 (WUH)	24.5	27.3	29.8
	福州 (FOC)	3.4	8.5	15.5
	上海虹桥 (SEA)	0.0	3.8	7.6
	西安 (XIY)	47.1	45.6	44.1
	南京 (NKG)	21.8	36.1	18.1
	杭州 (HGH)	14.1	19.3	6.9
亚洲 (4)	首尔仁川 (ICN)	62.4	74.9	33.3
	曼谷 (BKK)	10.8	81.3	42.5
	新加坡 (SIN)	1.2	11.0	16.5
	吉隆坡 (KUL)	1.3	1.3	29.8
欧洲和北美 (8)	法兰克福 (FRA)	34.5	16.3	0.2
	阿姆斯特丹 (AMS)	36.4	5.8	—
	莫斯科 (SVO)	1.4	—	—
	巴黎戴高乐 (CDG)	17.2	11.9	1.0
	赫尔辛基 (HEL)	48.7	17.0	1.3
	伊斯坦布尔 (IST)	8.9	—	—
	芝加哥奥黑尔 (ORD)	4.0	9.4	—
	旧金山 (SFO)	14.4	9.5	—
中东 (2)	迪拜 (DXB)	16.3	0.03	0.02
中东 (2)	多哈 (DOH)	30.5	5.3	—

三大机场	溢出流向机场	共同服务主要国际市场	市场重叠率 (%)	国际中转乘客量变化(2017年 VS 2010年, %)
北京首都	厦门	中国-北美	44.0	206.9
	曼谷	中国-西欧	10.8	123.1
	迪拜	中国-西欧	16.3	61.9
上海浦东	香港	中国-东南亚	36.2	32.9
	乌鲁木齐	中国-东欧、南亚	12.8	106.4
	吉隆坡	中国-东南亚	1.3	129.5
	首尔	中国-东北亚、北美、东南亚	74.9	15.1
广州白云	香港	中国-东南亚	60.6	32.9
	武汉	中国-东南亚	29.8	239.2
	西安	中国-西欧	44.1	527.0
	首尔	中国-东南亚、北美	33.3	15.1
	吉隆坡	中国-东南亚、南亚	29.8	129.5
	伊斯坦布尔	中国-西欧	0.0	255.5
	新加坡	中国-东南亚、南亚	16.5	77.2
	赫尔辛基	中国-西欧	1.3	58.9

枢纽机场的国际中转客流拥堵溢出效应研究

Research on congestion spillover effects of international transfer traffic on hub airports

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