Please wait a minute...
浙江大学学报(理学版)  2020, Vol. 47 Issue (2): 223-230    DOI: 10.3785/j.issn.1008-9497.2020.02.013
城市科学     
基于加权流量介数中心性的路网脆弱性分析——以无锡市为例
杜佳昕1,2, 张丰1,2, 杜震洪1,2, 刘仁义1,2
1.浙江大学 浙江省资源与环境信息系统重点实验室,浙江 杭州 310028
2.浙江大学 地理信息科学研究所, 浙江 杭州 310027
Assessing vulnerability of road networks based on traffic flow betweenness centrality: A case study in Wuxi
DU Jiaxin1,2, ZHANG Feng1,2, DU Zhenhong1,2, LIU Renyi1,2
1.Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China
2.Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China
 全文: PDF(3189 KB)   HTML  
摘要: 针对当前路网脆弱性研究中缺乏对真实交通状况考量的问题,在复杂网络理论的基础上,结合交通流量信息,提出了基于加权流量介数中心性的路网脆弱性分析方法。首先计算路网拓扑抽象图中各节点的最短路径介数中心性,然后使用流量数据对相应区域最短路径介数中心性加权,综合得到最终的脆弱性指标结果。以无锡市为例,对其实际交通路网脆弱性进行了计算,结果表明,该方法能综合反映静态全局路网结构与动态局部通行信息和现实交通情景下的路网脆弱性。
关键词: 路网脆弱性轨迹数据交通    
Abstract: Road networks are vulnerable to many events. Thus protecting the essential part of roads is important in rescue and emergency cases. Traffic Flow Betweenness Centrality (TFBC) was introduced to the study of network vulnerability considering both static network structure and dynamic demands of traffic. Taking Wuxi city as a case, using road information from the open street map and taxi trajectory data, the vulnerability of city road network was estimated. Vulnerability maps and statistics are presented to compare with the results by other vulnerability compute methods for validation and verification. The results show that:(1) Both TFBC and static network structure analysis gave high scores of vulnerability to streets that are near metropolitan areas. Those streets are the main vessels connecting different parts of the city. (2) TFBC identified important city center hubs, such as train stations, hospitals, flyovers as vulnerable. However, the vulnerability of these essential parts was underestimated by the static network structure analysis. (3) Nodes were assigned to low vulnerability in rural areas by TFBC. In contrast, static network structure analysis assigned high value to roads in villages, especially those which were connected to the metropolitan area. It seemed not reasonable since few people are using those road. The method of TFBC in road network research can effectively reveal the level of vulnerability by considering both static network structure and dynamic demands of traffic, which is of great significance to understanding the runtime pattern of road networks. The data source of TFBC is not limited to taxi trajectories, but can also use trajectory data generated by cell phones, bicycles and other motor vehicles. With more trajectory data provided in future studies, the results calculated by TFBC will be more accurate. Our research results are of great value to the related policy-making in urban transportation planning and management.
Key words: road networks    vulnerability    trajectory data    traffic
收稿日期: 2018-12-23 出版日期: 2020-03-25
CLC:  F511.99  
基金资助: 国家自然科学基金资助项目(41471313,41671391); 国家重点研发计划专项(2018YFB0505000,2016YFC0803105).
通讯作者: ORCID: https://orcid.org/0000-0003-1475-8480,E-mail:zfcarnation@zju.edu.cn.     E-mail: zfcarnation@zju.edu.cn
作者简介: 杜佳昕(1994—),ORCID: https://orcid.org/0000-0003-4116-1612,男,硕士研究生,主要从事高性能地理信息系统研究.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
杜佳昕
张丰
杜震洪
刘仁义

引用本文:

杜佳昕, 张丰, 杜震洪, 刘仁义. 基于加权流量介数中心性的路网脆弱性分析——以无锡市为例[J]. 浙江大学学报(理学版), 2020, 47(2): 223-230.

DU Jiaxin, ZHANG Feng, DU Zhenhong, LIU Renyi. Assessing vulnerability of road networks based on traffic flow betweenness centrality: A case study in Wuxi. Journal of Zhejiang University (Science Edition), 2020, 47(2): 223-230.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2020.02.013        https://www.zjujournals.com/sci/CN/Y2020/V47/I2/223

1 刘承良,余瑞林,曾菊新,等. 武汉城市圈城乡道路网的空间结构复杂性[J]. 地理科学, 2012, 32(4):426-433. DOI:10.13249/j.cnki.sgs.2012.04.005 LIUC L, YUR L, ZENGJ X, et al. Complexity of spatial structure on the urban-rural road network in Wuhan metropolitan area[J]. Scientia Geographica Sinica, 2012, 32(4): 426-433.
2 李彤玥. 基于“暴露—敏感—适应”的城市脆弱性空间研究—以兰州市为例[J]. 经济地理, 2017,37(3): 86-95. LIT Y. Spatial vulnerability based on the framework of the exposure-sensitivity-adaptive capacity: A case study of Lanzhou[J]. Economic Geography, 2017,37(3): 86-95.
3 JENELIUSE, PETERSENT, MATTSSONL G. Importance and exposure in road network vulnerability analysis[J]. Transportation Research Part A : Policy & Practice, 2006, 40(7): 537-560.DOI:10.1016/j.tra.2005.11.003
4 MURRAYA T, MATISZIWT C, GRUBESICT H. A methodological overview of network vulnerability analysis[J]. Growth and Change, 2008, 39(4): 573-592.DOI:10.1111/j.1468-2257.2008.00447.x
5 MURRAYA T. An overview of network vulnerability modeling approaches[J]. Geo Journal, 2013, 78(2): 209-221.DOI:10.1007/s10708-011-9412-z
6 BERDICAK. 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
7 CANTILLOV, MACEAL F, JALLERM. Assessing vulnerability of transportation networks for disaster response operations[J]. Networks & Spatial Economics, 2019,19(1):243-273.DOI:10.1007/s11067-017-9382-x
8 赖君毅. 道路交通网络脆弱性评估验证及其可视化方法研究[D]. 武汉:华中科技大学, 2013. LAIJ Y. Road Traffic Network Vulnerability Assessment, Validation and Its Visualization Method[D]. Wuhan: Huazhong University of Science and Technology, 2013.
9 张勇,屠宁雯,姚林泉. 城市道路交通网络脆弱性辨识方法[J]. 中国公路学报, 2013, 26(4): 154-161.DOI:10.19721/j.cnki.1001-7372.2013.04.021 ZHANGY, TUN Y, YAOL Q. Urban road traffic network vulnerability identification method[J]. China Journal of Highway and Transport, 2013, 26(4): 154-161. DOI:10.19721/j.cnki.1001-7372.2013.04.021
10 VILJOENN M, JOUBERTJ W. The road most travelled: The impact of urban road infrastructure on supply chain network vulnerability[J]. Networks & Spatial Economics, 2018, 18(1): 85-113.DOI:10.1007/s11067-017-9370-1
11 DEMŠARU, ŠPATENKOVÁO, VIRRANTAUSK. Centrality measures and vulnerability of spatial networks[J]. Proceedings Intelligent Human Computer Systems for Crisis Response and Management , 2007:201-209.
12 ROSVALLM, TUSINAA, MINNHAGENP, et al. Networks and cities: An information perspective[J]. Physical Review Letters, 2005, 94(2): 028701.
13 陆化普,石冶. 三亚市道路网络复杂性研究[J]. 公路工程, 2010, 35(6): 18-21,25. LUH P, SHIZ. Research of complexity of urban road networks of Sanya[J]. Highway Engineering, 2010, 35(6): 18-21,25.
14 WANGL, WEIL, ZHANGY Z, et al. Node importance assessment of traffic complex network based on c-means clustering[J]. Advanced Materials Research, 2011(211-212): 963-967.
15 GRIFFITHD A, CHUNY W. Spatial autocorrelation in spatial interactions models: Geographic scale and resolution implications for network resilience and vulnerability[J]. Networks & Spatial Economics, 2015, 15(2): 337-365.DOI:10.1007/s11067-014-9256-4
16 BRANDESU, FLEISCHERD. Centrality measures based on current flow[J]. Lecture Notes in Computer Science, 2005(3404): 533-544.DOI:10.1007/978-3-540-31856-9_44
17 叶青,彭其渊. 考虑客流拥堵的城轨网络脆弱性评估[J]. 计算机应用研究, 2016, 33(10): 2923-2925, 2945. YEQ, PENGQ Y. Vulnerability analysis of urban rail transit network based on passenger crowding[J]. Application Research of Computers, 2016, 33(10): 2923-2925,2945.
18 刘海旭,荣新. 基于路段流量分析的路网脆弱性研究[J]. 综合运输, 2017, 39(11): 63-67. LIUH X, RONGX. Road traffic network vulnerability study based on link flow analysis[J]. China Transportation Review, 2017, 39(11): 63-67.
19 付跃龙. 基于级联失效的道路网络脆弱性研究[D]. 武汉:华中科技大学, 2015. FUY L. Vulnerability of Urban Road Network Based on Cascade Defense[D]. Wuhan: Huazhong University of Science and Technology, 2015.
20 KERNERB S, DEMIRC, HERRTWICHR G, et al. Traffic state detection with floating car data in road networks[C]//Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2005: 44-49. DOI:10.1109/itsc.2005.1520133
21 高中华,李满春,陈振杰,等. 城市道路网络的小世界特征研究[J]. 地理与地理信息科学, 2007, 23(4): 97-101. GAOZ H, LIM C, CHENZ J, et al. Research on small world characteristic of urban road network[J]. Geography and Geo-Information Science, 2007, 23(4): 97-101.
22 FREEMANL C. Centrality in social networks conceptual clarification[J]. Social Networks, 1978, 1(3): 215-239. DOI:10.1016/0378-8733(78)90021-7
23 FREEMANL C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1): 35-41. DOI:10.2307/3033543
24 胡一竑,吴勤旻,朱道立. 城市道路网络的拓扑性质和脆弱性分析[J]. 复杂系统与复杂性科学, 2009, 6(3): 69-76. DOI:10.13306/j.1672-3813.2009.03.008 HUY H, WUQ W, ZHUD L. Topological properties and vulnerability analysis of spatial urban street networks[J]. Complex Systems and Complexity Science, 2009, 6(3): 69-76. DOI:10.13306/j.1672-3813.2009.03.008
25 ÁLVAREZ-MIRANDAE, CANDIA-VÉJARA, CARRIZOSAE, et al. Vulnerability assessment of spatial networks: Models and solutions[C]// International Symposium on Combinatorial Optimization. Switzerland: Springer International Publishing, 2014:433-444. DOI:10.1007/978-3-319-14115-2_37
26 KHOSROWD. Density estimation for statistics and data analysis[J]. Technometrics, 1986, 29(4):495.
27 BRANDESU. A faster algorithm for betweenness centrality[J]. Journal of Mathematical Sociology, 2001, 25(2): 163-177.DOI:10.1080/0022250x.2001.9990249
28 无锡市统计局.无锡统计年鉴[M].北京:中国统计出版社,2018. Wuxi Bureau of Statistic. Wuxi Statistical Year Book[M]. Beijing: China Statistic Press, 2018.
29 刘张,王心迪,闫小勇. 面向复杂城市道路网络的GPS轨迹匹配算法[J]. 电子科技大学学报, 2016, 46(6): 1008-1013. LIUZ, WANGX D, YANX Y. Map-matching algorithm for gps trajectories in complex urban road networks[J]. Journal of University of Electronic Science and Technology of China, 2016, 46(6): 1008-1013.
30 PETTYK F, BICKELP, OSTLANDM, et al. Accurate estimation of travel times from single-loop detectors [J]. Transportation Research Part A: Policy & Practice, 1998,32(1): 1-17.DOI:10.1016/s0965-8564(97)00015-3
31 KESTINGA, TREIBERM. Traffic Flow Dynamics: Data, Models and Simulation[M]. Heidelberg: Springer, 2013.
32 刘丽,张丰,杜震洪,等. 基于深圳市出租车轨迹数据的高效益寻客策略研究[J]. 浙江大学学报(理学版), 2018, 45(1): 82-91.DOI:10.3785/j.issn.1008-9497.2018.01.013 LIUL, ZHANGF, DUZ H, et al. The analysis of high profitable strategy for seeking passengers based on taxi GPS trajectory data of Shenzhen city[J]. Journal of Zhejiang University(Science Edition), 2018, 45(1): 82-91.DOI:10.3785/j.issn.1008-9497.2018.01.013
[1] 王钰帆,白强,孙虎. 2000—2020年汾河流域生态脆弱性时空演变与驱动因素[J]. 浙江大学学报(理学版), 2023, 50(5): 607-618.
[2] 邓一凌. TOD中“T”与“D”因素对小汽车出行距离的减量影响[J]. 浙江大学学报(理学版), 2022, 49(3): 376-383.
[3] 王颖志, 沈雅婕, 王立君. 基于改进兴趣度度量与Apriori算法的交通事故多发点成因分析[J]. 浙江大学学报(理学版), 2021, 48(3): 349-355.
[4] 卢家品, 罗月童, 黄兆嵩, 张延孔, 陈为. 基于排名学习和多源信息的地图匹配方法[J]. 浙江大学学报(理学版), 2020, 47(1): 27-35.
[5] 刘尧, 王颖志, 王立君, 张丰, 杜震洪, 刘仁义. 交通事故的时空热点分析[J]. 浙江大学学报(理学版), 2020, 47(1): 52-59.
[6] 冯友建, 陈天一. 基于SEM模型的轨道交通对住宅价格的空间效应——以杭州市为例[J]. 浙江大学学报(理学版), 2020, 47(1): 115-122.
[7] 刘丽, 张丰, 杜震洪, 刘仁义, 贾玉杰. 基于深圳市出租车轨迹数据的高效益寻客策略研究[J]. 浙江大学学报(理学版), 2018, 45(1): 82-91.
[8] 刘军胜, 马耀峰. 1995~2014年西北5省旅游流与区域交通的耦合过程与格局[J]. 浙江大学学报(理学版), 2017, 44(5): 606-615.
[9] 邵江, 胡晓晓, 白宝钢, 王长红, 朱晨杰, 陈军华. 波阵面视角下的车辆拥堵分析[J]. 浙江大学学报(理学版), 2016, 43(4): 481-491.
[10] 吴潘, 吴晋峰, 周芳如, 李佳丽. 目的地内部旅游交通通达性评价方法研究——以西安为例[J]. 浙江大学学报(理学版), 2016, 43(3): 345-356.
[11] 王绍民,黄富泉,卢山鹰,沈惠根,赵道木 . 激光成珠大小镶嵌高效率逆反射列阵膜 [J]. 浙江大学学报(理学版), 2000, 27(1): 105-108.