考虑互补效应的城市群多模式客运网络鲁棒性

doi:10.3785/j.issn.1008-973X.2024.02.017

浙江大学学报(工学版)

2024, Vol. 58

Issue (2): 388-398 DOI: 10.3785/j.issn.1008-973X.2024.02.017

交通工程、土木工程

考虑互补效应的城市群多模式客运网络鲁棒性

马飞(

),委笑琳,孙启鹏*(

),刘擎,苟慧艳

长安大学经济与管理学院，陕西西安 710064

Robustness of multimodal passenger transport network in urban agglomeration considering complementary effect

Fei MA(

),Xiaolin WEI,Qipeng SUN*(

),Qing LIU,Huiyan GOU

School of Economics and Management, Chang’an University, Xi’an 710064, China

全文: PDF(2456 KB) HTML

摘要：

为了分析城市群客运网络对突发事件的应对能力，从多种交通模式互补视角，基于复杂网络理论构建城市群多模式客运网络模型，应用引力模型建立节点失效后网络性能计算的规则，对城市群多模式客运网络的鲁棒性进行测度. 引入基于鲁棒性的动态互补强度指标，对客运网络间的互补效应（CE）进行量化. 对是否考虑CE时的鲁棒性进行对比，分析CE对鲁棒性的提升程度. 以关中平原城市群为例进行实例研究，结果表明，在城市群多模式客运网络中，普铁网应对突发事件时的鲁棒性最强，公路网最弱. 无论在何种攻击策略下，考虑CE时，城市群多模式客运网络的鲁棒性均有明显的提升，高铁网、普铁网和公路网的敏感度分别下降了19.1%、29.3%和22.0%.

关键词： 城市群; 多模式客运网络; 复杂网络理论; 鲁棒性; 互补效应

Abstract:

A multimodal passenger transport network model of urban agglomeration was constructed based on complex network theory from the perspective of multiple complementary traffic modes in order to analyze the response ability of urban agglomeration passenger transport networks to emergencies. The gravity model was applied to establish rules for network performance calculation after node failure, and the robustness of the multimodal passenger transport network of urban agglomeration was measured. A dynamic complementary strength index based on robustness was introduced to quantify the complementary effect (CE) between passenger transport networks. The robustness when considering CE was compared, and the degree to which CE improves the robustness value was analyzed. A case study of Guanzhong Plain urban agglomeration was conducted. Results showed that the general railway network was the most robust to unexpected events, and the highway network was the least robust to unexpected events among the multimodal passenger transport networks in urban agglomerations. The robustness of the urban agglomeration multimodal passenger network was significantly improved when CE was considered regardless of the attack strategy. The sensitivity of high-speed rail network, rail network and highway network was decreased by 19.1%, 29.3%和22.0%, respectively.

Key words: urban agglomeration multimodal passenger transport network complex network theory robustness complementary effect

收稿日期: 2023-05-31 出版日期: 2024-01-23

CLC:

U 125

基金资助: 国家自然科学基金资助项目（72104034）；陕西省自然科学基础研究计划资助项目（2022JM-423，2022JM-426）；西安市社会科学规划基金重大资助项目（211423220227）.

通讯作者: 孙启鹏 E-mail: mafeixa@chd.edu.cn;sunqip@chd.edu.cn

作者简介: 马飞（1979—），男，教授，博导，从事交通基础设施韧性的研究. orcid.org/0000-0003-1398-5972. E-mail：mafeixa@chd.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	马飞
	委笑琳
	孙启鹏
	刘擎
	苟慧艳

引用本文:

马飞,委笑琳,孙启鹏,刘擎,苟慧艳. 考虑互补效应的城市群多模式客运网络鲁棒性[J]. 浙江大学学报(工学版), 2024, 58(2): 388-398.

Fei MA,Xiaolin WEI,Qipeng SUN,Qing LIU,Huiyan GOU. Robustness of multimodal passenger transport network in urban agglomeration considering complementary effect. Journal of ZheJiang University (Engineering Science), 2024, 58(2): 388-398.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.02.017 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I2/388

图 1 交通网络模型构建方法的示意图

图 2 关中平原城市群多模式客运网络拓扑图

图 3 关中平原城市群的客运网络及节点互补关系

表 1 关中平原城市群客运网络拓扑指标的统计

表 2 关中平原城市群各节点失效后的客运网络鲁棒性

图 4 关中平原城市群节点失效后客运网络的性能值

图 5 关中平原城市群多模式客运网络节点动态互补强度

图 6 不同攻击策略下关中平原城市群多模式客运网络的鲁棒性变化

图 7 随机攻击下$\omega $对${G_{2 - 2}}$鲁棒性的影响

图 8 不考虑CE时不同攻击策略下关中平原城市群子网的敏感度变化曲线

图 9 考虑CE时不同攻击策略下关中平原城市群子网的敏感度变化曲线

1	LI M M, GUO R Z, LI Y, et al Distribution characteristics of the transportation network in China at the county level[J]. IEEE Access, 2019, 7: 49251- 49261 doi: 10.1109/ACCESS.2019.2910299
2	HAN J, GAO M, SUN Y W Research on the measurement and path of urban agglomeration growth effect[J]. Sustainability, 2019, 11 (19): 5179 doi: 10.3390/su11195179
3	马书红, 武亚俊, 陈西芳城市群多模式交通网络结构韧性分析: 以关中平原城市群为例[J]. 清华大学学报: 自然科学版, 2022, 62 (7): 1228- 1235 MA Shuhong, WU Yajun, CHEN Xifang Structural resilience of multimodal transportation networks in urban agglomerations: a case study of Guanzhong Plain urban agglomeration network[J]. Journal of Tsinghua University: Science and Technology, 2022, 62 (7): 1228- 1235
4	MA F, SHI W J, YUEN K F, et al Exploring the robustness of public transportation for sustainable cities: a double-layered network perspective[J]. Journal of Cleaner Production, 2020, 265: 121747 doi: 10.1016/j.jclepro.2020.121747
5	OUYANG M, PAN Z Z, HONG L, et al Vulnerability analysis of complementary transportation systems with applications to railway and airline systems in China[J]. Reliability Engineering and System Safety, 2015, 142: 248- 257 doi: 10.1016/j.ress.2015.05.013
6	马书红, 陈西芳, 武亚俊, 等基于可达性的城市群交通网络公平性分析[J]. 交通运输系统工程与信息, 2022, 22 (6): 51- 59 MA Shuhong, CHEN Xifang, WU Yajun, et al Equity analysis of transportation networks in urban agglomerations based on accessibility[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22 (6): 51- 59
7	李成兵, 李云飞, 吴鹏基于多智能体的城市群客运网络脆弱性动态仿真[J]. 系统仿真学报, 2023, 35 (6): 1183- 1190 LI Chengbing, LI Yunfei, WU Peng Dynamic simulation of urban agglomeration passenger transport network vulnerability based on multi-agent[J]. Journal of System Simulation, 2023, 35 (6): 1183- 1190
8	LIU S, YIN C S, CHEN D J, et al Cascading failure in multiple critical infrastructure interdependent networks of syncretic railway system[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (6): 5740- 5753 doi: 10.1109/TITS.2021.3057404
9	MA F, LIU F, YUEN K F, et al Cascading failures and vulnerability evolution in Bus-Metro complex bilayer networks under rainstorm weather conditions[J]. International Journal of Environmental Research and Public Health, 2019, 16 (3): 329 doi: 10.3390/ijerph16030329
10	侯兰功, 孙继平复杂网络视角下的成渝城市群网络结构韧性演变[J]. 世界地理研究, 2022, 31 (3): 561- 571 HOU Langong, SUN Jiping Evalution of network structure resilience of Chengdu-Chongqing urban agglomeration from the perspective of complex networks[J]. World Regional Studies, 2022, 31 (3): 561- 571 doi: 10.3969/j.issn.1004-9479.2022.03.2020538
11	肖明辉基于区域攻击的城市公共交通互补网络脆弱性分析[J]. 信息通信, 2018, (8): 1- 5 XIAO Minghui The vulnerability analysis of urban complementary transit network under spatially localized failures[J]. Information and Communications, 2018, (8): 1- 5
12	路庆昌, 崔欣, 徐标, 等公交接驳场景下轨道交通网络脆弱性研究[J]. 中国安全科学学报, 2021, 31 (8): 141- 146 LU Qingchang, CUI Xin, XU Biao, et al Vulnerability research of rail transit network under bus connection scenarios[J]. China Safety Science Journal, 2021, 31 (8): 141- 146
13	YANG H H, AN S Robustness evaluation for multi-subnet composited complex network of urban public transport[J]. Alexandria Engineering Journal, 2021, 60 (2): 2065- 2074 doi: 10.1016/j.aej.2020.12.016
14	HONG L, ZHONG X, OUYANG M, et al Vulnerability analysis of public transit systems from the perspective of urban residential communities[J]. Reliability Engineering and System Safety, 2019, 189: 143- 156 doi: 10.1016/j.ress.2019.04.018
15	LI T, RONG L L Spatiotemporally complementary effect of high-speed rail network on robustness of aviation network[J]. Transportation Research Part A-Policy and Practice, 2022, 155: 95- 114 doi: 10.1016/j.tra.2021.10.020
16	LI T, RONG L L, YAN K S Vulnerability analysis and critical area identification of public transport system: a case of high-speed rail and air transport coupling system in China[J]. Transportation Research Part A: Policy and Practice, 2019, 127: 55- 70 doi: 10.1016/j.tra.2019.07.008
17	FANG C, CHU Y Z, FU H R, et al On the resilience assessment of complementary transportation networks under natural hazards[J]. Transportation Research Part D-Transport and Environment, 2022, 109: 103331 doi: 10.1016/j.trd.2022.103331
18	SUN X Q, WANDELT S Complementary strengths of airlines under network disruptions[J]. Safety Science, 2018, 103: 76- 87 doi: 10.1016/j.ssci.2017.11.010
19	刘承良, 殷美元, 黄丽基于多中心性分析的中国交通网络互补性的空间格局[J]. 经济地理, 2018, 38 (10): 21- 28 LIU Chengliang, YIN Meiyuan, HUANG Li Spatial pattern of complementarity between the three transport networks in China based on multiple centrality assessments[J]. Economic Geography, 2018, 38 (10): 21- 28
20	LI T, RONG L L A comprehensive method for the robustness assessment of high-speed rail network with operation data: a case in China[J]. Transportation Research Part A: Policy and Practice, 2020, 132: 666- 681 doi: 10.1016/j.tra.2019.12.019
21	马飞, 汪倩倩, 李永平, 等生态安全约束下基于“交通-信息”流的城市群空间网络结构[J]. 长安大学学报: 自然科学版, 2022, 42 (5): 86- 95 MA Fei, WANG Qianqian, LI Yongping, et al Spatial network structure of urban agglomeration based on traffic-information flow under ecological security constraints[J]. Journal of Chang'an University: Natural Science Edition, 2022, 42 (5): 86- 95
22	项昀, 徐铖铖, 于维杰, 等基于人口迁徙大数据的城市对外交通客运方式优势出行距离研究[J]. 交通运输系统工程与信息, 2020, 20 (1): 241- 246 XIANG Yun, XU Chengcheng, YU Weijie, et al Dominant trip distance of urban external passenger transport mode based on big data of migration.[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20 (1): 241- 246
23	胡钢, 牛琼, 王琴, 等时序多层网络熵值结构洞节点重要性建模[J]. 浙江大学学报: 工学版, 2023, 57 (4): 719- 725 HU Gang, NIU Qiong, WANG Qin, et al Modeling of node importance in entropy-value structured hole of temporal multilayer network[J]. Journal of Zhejiang University: Engineering Science, 2023, 57 (4): 719- 725
24	周传华, 操礼春, 周家亿, 等图卷积融合计算时效网络节点重要性评估分析[J]. 浙江大学学报: 工学版, 2023, 57 (5): 930- 938 ZHOU Chuanhua, CAO Lichun, ZHOU Jiayi, et al Identification of critical nodes in temporal networks based on graph convolution union computing[J]. Journal of Zhejiang University: Engineering Science, 2023, 57 (5): 930- 938
25	王永岗, 王龙健, 刘志岗, 等多模式复合交通网脆弱性测度[J]. 交通运输工程学报, 2023, 23 (1): 195- 207 WANG Yonggang, WANG Longjian, LIU Zhigang, et al Vulnerability metrics of multimodal composite transportation network[J]. Journal of Traffic and Transportation Engineering, 2023, 23 (1): 195- 207 doi: 10.19818/j.cnki.1671-1637.2023.01.015

[1]	郭策,曾志文,朱鹏铭,周智千,卢惠民. 基于图卷积模仿学习的分布式群集控制[J]. 浙江大学学报(工学版), 2022, 56(6): 1055-1061.
[2]	陈小波,陈玲,梁书荣,胡煜. 重尾非高斯定位噪声下鲁棒协同目标跟踪[J]. 浙江大学学报(工学版), 2022, 56(5): 967-976.
[3]	沈麒,赵琰,周晓炜,袁晓冉. 结合结构与梯度的图像哈希算法[J]. 浙江大学学报(工学版), 2020, 54(8): 1525-1533.
[4]	隋昊,覃高峰,崔祥波,陆新江. 基于误差均值与方差最小化的鲁棒T-S模糊建模方法[J]. 浙江大学学报(工学版), 2019, 53(2): 382-387.
[5]	龙奋杰,郑龙飞,石朗,陈子晏. 基于公路大数据的黔中城市群一体化研究[J]. 浙江大学学报(工学版), 2019, 53(2): 307-314.
[6]	杨春宁, 方家为, 李春, 葛晖. 基于稳定性判据的高超声速复合控制方法[J]. 浙江大学学报(工学版), 2017, 51(2): 422-428.
[7]	李滔, 王士同. 增量式0阶TSK模糊分类器及鲁棒改进[J]. 浙江大学学报(工学版), 2017, 51(10): 1901-1911.
[8]	杨慧琳, 黄智刚, 刘久文, 杜元锋. 基于核模糊C均值指纹库管理的WIFI室内定位方法[J]. 浙江大学学报(工学版), 2016, 50(6): 1126-1133.
[9]	赵婵媛, 陆志强, 崔维伟. 考虑随机故障的流水线调度问题前摄优化方法[J]. 浙江大学学报(工学版), 2016, 50(4): 641-649.
[10]	王卫东,李俊杰,王京,傅庆湘,康文泓. 基于未确知测度的公路交通效率评价[J]. 浙江大学学报(工学版), 2016, 50(1): 48-54.
[11]	朱光明, 蒋荣欣, 周凡, 田翔, 陈耀武. 带测量偏置估计的鲁棒卡尔曼滤波算法[J]. 浙江大学学报(工学版), 2015, 49(7): 1343-1349.
[12]	马腾, 赵兴忠, 高博青, 吴慧. 自由曲面形状和拓扑联合优化研究[J]. 浙江大学学报(工学版), 2015, 49(10): 1946-1951.
[13]	张成,李志安,高博青,董石麟. 基于H∞理论的网壳结构鲁棒性分析[J]. J4, 2013, 47(5): 818-823.
[14]	单艳玲, 高博青. 基于连续体拓扑优化的网壳结构鲁棒构型分析[J]. J4, 2013, 47(12): 2118-2124.
[15]	王秀君, 胡协和. 一种改进的单神经元PID控制策略[J]. J4, 2011, 45(8): 1498-1501.

Viewed

Full text

Abstract

Cited

Shared

Discussed