中国内陆地区出境交通系统脆弱性特征及障碍因子分析

doi:10.16265/j.cnki.issn1003-3033.2026.01.0824

摘要/Abstract

摘要：

为优化内陆出境交通系统布局,提升跨境运输韧性,构建“敏感性-应对性”双维度评价指标体系,综合集对分析模型、空间马尔可夫链与障碍度模型,系统解析中国内陆225个城市的脆弱性动态特征及其驱动机制。结果表明:脆弱性呈现显著的多极分化与空间俱乐部收敛特征,随时间演化脆弱性整体逐年降低,且由两极化向多极化演变,绝对差异减小。脆弱性具有显著空间自相关特征和空间溢出效应,形成“高脆弱性沿边集聚,低脆弱性以城市群为中心扩散”的空间格局,邻域背景通过路径依赖制约脆弱性演化方向,邻域低脆弱性环境可使城市脆弱性向下转移概率提升64.5%。货运周转量和进出口贸易总额障碍因子的作用逐年减弱;内陆-口岸公路最短旅行时间障碍效应从相对稳定逐渐转为持续上升;城市枢纽等级和连接度对非枢纽城市脆弱性构成显著制约。

关键词: 内陆地区, 出境交通系统脆弱性, 时空演化, 空间溢出效应, 障碍因子

Abstract:

To optimize the layout of the outbound transportation system and enhance system resilience, a "Sensitivity-Response" dual-dimensional evaluation index system was constructed. The comprehensive methods including set pair analysis, spatial Markov chain, and obstacle degree model were used to analyze the dynamic characteristics and driving mechanisms of vulnerability in 225 inland cities in China. The findings reveal that vulnerability demonstrates significant multi-polarization and spatial club convergence. The overall vulnerability decreases annually, evolving from bi-polarization to multi-polarization, with a reduction of absolute difference. Vulnerability exhibits significant spatial auto-correlation and spillover effects, forming a spatial pattern where "high-vulnerability is clustered along the borders, while low-vulnerability diffuses from urban agglomerations". A low-vulnerability neighborhood environment increases the probability of a city's vulnerability transferring downward by 64.5%. The obstructive effects of both freight turnover volume and total import-export trade value weaken annually. In contrast, the hindering effect of shortest inland-port road travel time shifts from a relatively stable to a consistent increase. Furthermore, city hub level and connectivity impose significant constraints on vulnerability of non-hub cities.

Key words: inland areas, vulnerability of outbound transportation systems, spatio-temporal evolution, spatial spillover effects, obstacle factors

中图分类号:

X951交通运输安全

李杰梅, 杨颖, 张远雄. 中国内陆地区出境交通系统脆弱性特征及障碍因子分析[J]. 中国安全科学学报, 2026, 36(1): 174-181.

LI Jiemei, YANG Ying, ZHANG Yuanxiong. Analysis of vulnerability characteristics and obstacle factors of outbound transportation systems in China's inland areas[J]. China Safety Science Journal, 2026, 36(1): 174-181.

图/表 8

表1

表2

图1

图2

图3

表3

表4

图4

参考文献 20

[1]	SPAANS M, WATEROUT B. Building up resilience in cities Worldwide-rotterdam as participant in the 100 resilient cities programme[J]. Cities, 2017, 61: 109-116. doi: 10.1016/j.cities.2016.05.011
[2]	NICHOLSON A, DU Zhenping. Degradable transportation systems: an integrated equilibrium model[J]. Transportation Research Part B: Methodological, 1997, 31(3): 209-223. doi: 10.1016/S0191-2615(96)00022-7
[3]	BERDICA K. An introduction to road vulnerability: what has been done, is done and should be done[J]. Transport Policy, 2002, 9(2): 117-127. doi: 10.1016/S0967-070X(02)00011-2
[4]	DUAN Yingying, FENG Lu. Robustness of city road networks at different granularities[J]. Physica A: Statistical Mechanics & its Applications, 2014, 411: 21-34.
[5]	尹洪英, 徐丽群. 道路交通网络脆弱性评估研究现状与展望[J]. 交通运输系统工程与信息, 2010, 10(3): 7-13.
	YIN Hongying, XU Liqun. Vulnerability assessment of transportation road networks[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(3): 7-13.
[6]	杨露萍, 钱大琳. 道路交通网络脆弱性研究[J]. 交通运输系统工程与信息, 2012, 12(1): 105-110.
	YANG Luping, QIAN Dalin. Vulnerability analysis of road networks[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1): 105-110.
[7]	TAYLOR M A P. Remoteness and accessibility in the vulnerability analysis of regional road networks[J]. Transportation Research Part A: Policy & Practice, 2012, 46(5): 761-771.
[8]	吴泓润, 覃俊, 郑波尽. 基于代价的复杂网络抗攻击性研究[J]. 计算机科学, 2012, 39(8): 224-227.
	WU Hongrun, QIN Jun, ZHENG Bojin. Anti-attack ability bases on costs in complex networks[J]. Computer Science, 2012, 39(8): 224-227.
[9]	段东立, 吴俊, 邓宏钟, 等. 基于可调负载重分配的复杂网络级联失效模型[J]. 系统工程理论与实践, 2013, 33(1): 203-208. doi: 10.12011/1000-6788(2013)1-203
	DUAN Dongli, WU Jun, DENG Hongzhong, et al. Cascading failure model of complex networks based on tunable load redistribution[J]. Systems Engineering-Theory & Practice, 2013, 33(1): 203-208.
[10]	李大愚, 高光锐. 城市地铁网脆弱性分析[J]. 中国安全科学学报, 2016, 26(3): 157-161.
	LI Dayu, GAO Guangrui. Analysis of vulnerability of urban metro network[J]. China Safety Science Journal, 2016, 26(3): 157-161.
[11]	ZHANG Jianhua, HU Funian, WANG Shuliang, et al. Structural vulnerability and intervention of high-speed railway networks-science direct[J]. Physica A: Statistical Mechanics and its Applications, 2016, 462: 743-751. doi: 10.1016/j.physa.2016.06.132
[12]	于宝, 冯春, 朱倩, 等. 中国高速铁路网络脆弱性分析[J]. 中国安全科学学报, 2017, 27(9): 110-115. doi: 10.16265/j.cnki.issn1003-3033.2017.09.019
	YU Bao, FENG Chun, ZHU Qian, et al. Vulnerability analysis of China's high-speed railway network[J]. China Safety Science Journal, 2017, 27(9): 110-115. doi: 10.16265/j.cnki.issn1003-3033.2017.09.019
[13]	张欣, 李双菲, 孙代源. 中欧集装箱海铁复合运输网络脆弱性分析[J]. 交通信息与安全, 2023, 41(3): 48-58.
	ZHANG Xin, LI Shuangfei, SUN Daiyuan. Vulnerability analysis of China-Europe container seal-rail intermodal transport network[J]. Journal of Transportation Information and Safety, 2023, 41(3): 48-58.
[14]	MATTSSON L, JENELIUS E. Vulnerability and resilience of transport systems: a discussion of recent research[J]. Transportation Research Part A: Policy & Practice, 2015, 81: 16-34.
[15]	JENELIUS E, MATTSSON L. Road network vulnerability analysis: conceptualization, implementation and application[J]. Computers, Environment & Urban Systems, 2015, 49:136-147.
[16]	杨超, 涂颖菲, 陈小鸿. 交通网络需求脆弱性分析及应用[J]. 同济大学学报:自然科学版, 2012, 40(9): 1 329-1 332,1 341.
	YANG Chao, TU Yingfei, CHEN Xiaohong. Demand vulnerability analysis of transportation network and its application[J]. Journal of Tongji University: Natural Science Edition, 2012, 40(9): 1 329-1 332, 1 341.
[17]	王诺, 董玲玲, 吴暖, 等. 蓄意攻击下全球集装箱海运网络脆弱性变化[J]. 地理学报, 2016, 71(2): 293-303. doi: 10.11821/dlxb201602009
	WANG Nuo, DONG Lingling, WU Nuan, et al. The change of global container shipping network vulnerability under intentional attack[J]. Acta Geographica Sinica, 2016, 71(2): 293-303. doi: 10.11821/dlxb201602009
[18]	吴迪, 王宇鹏, 盛世杰, 等. “21世纪海上丝绸之路”集装箱海运网络的脆弱性变化[J]. 地理学报, 2022, 77(8): 2067-2082. doi: 10.11821/dlxb202208015
	WU Di, WANG Yupeng, SHENG Shijie, et al. Vulnerability changes of the maritime silk road container shipping network under intentional attacks[J]. Acta Geographica Sinica, 2022, 77(8): 2067-2082. doi: 10.11821/dlxb202208015
[19]	王列辉, 叶斐, 郑渊博. 中美集装箱航运网络格局演化与脆弱性评估[J]. 经济地理, 2020, 40(5): 136-144. doi: 10.15957/j.cnki.jjdl.2020.05.015
	WANG Liehui, YE Fei, ZHENG Yuanbo. The assessment of Sino-US container shipping network evolution and vulnerability[J]. Economic Geography, 2020, 40(5): 136-144. doi: 10.15957/j.cnki.jjdl.2020.05.015
[20]	杨晴青, 刘倩, 尹莎, 等. 秦巴山区乡村交通环境脆弱性及影响因素:以陕西省洛南县为例[J]. 地理学报, 2019, 74(6): 1236-1251. doi: 10.11821/dlxb201906012
	YANG Qingqing, LIU Qian, YIN Sha, et al. Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas: a case study of Luonan county in Shaanxi province[J]. Acta Geographica Sinica, 2019, 74(6): 1236-1251. doi: 10.11821/dlxb201906012

准则层	结构层	指标层	指标含义与属性	权重
敏感性指标	关键路径通行时长	内陆-口岸公路最短旅行时间S₂₁	反映内陆出境公路可达性(+)	0.089
	关键路径通行时长	内陆-口岸铁路最短旅行时间S₂₂	反映内陆出境铁路可达性(+)	0.069
	运输通道技术等级	铁路技术水平S₂₃	反映铁路发展水平(-)	0.044
		高速公路通车里程S₂₄	反映公路发展水平(-)	0.042
		民航及通用机场数量S₂₅	反映航空发展水平(-)	0.034
	货运需求	货运周转量S₂₆	反映货运总需求(+)	0.219
	货运需求	进出口贸易额S₂₇	反映出境货运需求(+)	0.238
应对性指标	政府干预	交通运输、仓储邮政业固定资产投资比重R₂₁	反映交通基础设施建设水平(+)	0.034
	政府干预	城市交通枢纽等级R₂₂	反映国家对交通战略支持力度(+)	0.059
	交通供给能力	路网密度R₂₃	反映城市内部交通的发展程度(+)	0.042
		城市连接度R₂₄	反映城市对外交通联系便利程度(+)	0.073
		交通工具拥有数R₂₅	反映城市交通工具供给情况(+)	0.023
	运输服务能力	交通运输、仓储邮政业从业人员数R₂₆	反映交通运输业人力供给水平(+)	0.022
	运输服务能力	互联网普及率R₂₇	反映交通信息化水平(+)	0.013

指数	年份
指数	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
敏感性	0.581	0.573	0.566	0.552	0.538	0.521	0.507	0.495	0.483	0.458	0.444	0.429
应对性	0.352	0.365	0.375	0.387	0.399	0.411	0.423	0.435	0.448	0.461	0.476	0.490
脆弱性	0.607	0.597	0.590	0.576	0.563	0.548	0.534	0.522	0.510	0.490	0.475	0.461

脆弱性等级	低	中	较高	高
低	0.986	0.014	0.000	0.000
中	0.110	0.886	0.001	0.000
较高	0.000	0.014	0.747	0.005
高	0.000	0.000	0.221	0.775

邻接类型	脆弱性等级	低	中	较高	高
低	低	1.000	0.000	0.000	0.000
	较低	0.182	0.818	0.000	0.000
	中等	0.000	0.400	0.000	0.000
	较高	0.000	0.000	0.720	0.000
	高	0.000	0.000	0.370	0.601
中	低	1.000	0.000	0.000	0.000
	较低	0.121	0.877	0.000	0.000
	中等	0.000	0.378	0.000	0.000
	较高	0.000	0.079	0.719	0.000
	高	0.000	0.000	0.400	0.600
较高	低	0.900	0.100	0.000	0.000
	较低	0.058	0.923	0.000	0.000
	中等	0.000	0.132	0.019	0.000
	较高	0.000	0.000	0.801	0.010
	高	0.000	0.000	0.194	0.806
高	低	1.000	0.000	0.000	0.000
	较低	0.040	0.940	0.000	0.000
	中等	0.000	0.000	0.000	0.000
	较高	0.000	0.000	0.962	0.000
	高	0.000	0.000	0.102	0.898