多源信息结合的雪灾交通风险评估研究

doi:10.12082/dqxxkx.2018.180191

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (11): 1571-1578.doi: 10.12082/dqxxkx.2018.180191

• 地球信息科学理论与方法 • 上一篇下一篇

多源信息结合的雪灾交通风险评估研究

隋琦^1,²(), 王瑛^1,², 李婷³, 刘庆爱³, 俞海洋^3,^*()

1. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
2. 北京师范大学减灾与应急管理研究院,北京 100875
3. 河北省气象灾害防御中心, 石家庄 050021

收稿日期:2018-04-16 修回日期:2018-08-10 出版日期:2018-11-20 发布日期:2018-11-20
通讯作者: 俞海洋 E-mail:suiqi@mail.bnu.edu.cn;8yhy8@sohu.com
作者简介:
作者简介：隋琦（1993-）,女,硕士,主要研究方向为灾害风险评估。E-mail: suiqi@mail.bnu.edu.cn
基金资助:
河北省气象与生态环境重点实验室开放基金项目（Z201603H）;国家重点研发计划项目(2016YFA0602403)

Application of Multi-source Information Fusion in the Traffic Risk Assessment of Snow Disaster

SUI Qi^1,²(), WANG Ying^1,², LI Ting³, LIU Qingai³, YU Haiyang^3,^*()

1. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
2. Academy of Disaster Reduction and Emergency Management, Civil Affairs and Ministry of Education, Beijing 100875, China
3. Defense Center of Meteorological disasters of Hebei Province, Shijiazhuang 050021, China

Received:2018-04-16 Revised:2018-08-10 Online:2018-11-20 Published:2018-11-20
Contact: YU Haiyang E-mail:suiqi@mail.bnu.edu.cn;8yhy8@sohu.com
Supported by:
Open Foundation Project from Key Laboratory of Meteorology and Ecological Environment of Hebei Province, No.Z201603H;National Key Research and Development Program of China, No.2016YFA0602403.

摘要/Abstract

摘要：

本研究将气象观测信息与网络信息相结合,提出了一种多源信息结合的雪灾交通风险评估方法：利用长时间气象观测数据分析雪灾的致灾强度时空特征,计算不同年遇型雪灾致灾强度;对门户网站、高速公路网站中节假日道路拥堵的新闻报道进行信息挖掘,获取高速公路暴露度;采用风险矩阵进行雪灾交通风险评估。将该方法应用于河北省,研究结果如下： ① 近5年来全省降雪有所下降,但长时间尺度来看,各地降雪呈波动变化;积雪深度高值区分布在张家口、承德、石家庄地区,但各年代间会有所变化;降雪次数高值区基本固定,在张家口康保、沽源、崇礼以及承德丰宁西北方向。② 暴露度级别高的路段是连接北京与上海、广州、哈尔滨等城市的高速公路,以及重要省市级联络线。③ 受致灾强度与暴露度的综合影响,河北省雪灾高风险路段集中在京港澳高速（石安G4）、京昆高速G5、京承高速G45、长深高速G25、张承高速G95等。这些路段必须做好雪灾风险防范措施。

关键词: 雪灾, 交通, 风险评估, 时空分布, 河北

Abstract:

This study proposed a method of traffic risk assessment of snow disaster based on multi-source information fusion including meteorological observation information and network information. Using the meteorological monitoring data in long time scale, the temporal and spatial characteristics of snowfall in Hebei province was analyzed with regard to frequency of snowfall and maximum of snow depth. The snow hazard intensity in different cases of return period events was calculated by function distribution fitting. Besides, we classified the exposure of highway in Hebei province by collecting the information of road congestion during holidays including Spring Festival and National Day from portal news websites, highway websites and so on. Finally, the risk matrix method was adopted to analyze the traffic risk of snow in Hebei province. That method was applied to Hebei province, and the study results were as follows: ① In recent 5 years, the snowfall in Hebei province has decreased. However, the snowfalls in different area fluctuated from decade to decade over a long-term scale. The high value areas of snow depth were located in Zhangjiakou, Chengde, and Shijiazhuang City, but they changed in different decades; the high frequency areas of snowfall were basically fixed, which was located in Kangbao, Guyuan and Chongli County in Zhangjiakou City, and the northwest of Fengning County in Chengde City. ② The sections of highway with high exposure were important provincial and city-level linking-up roads and the expressways which mainly connected Beijing with Shanghai, Guangzhou, Harbin and other major cities. ③ Affected by the comprehensive effects of hazard intensity and exposure, the high-risk sections of snow disaster were mainly concentrated in Beijing-Hong Kong-Macao Expressway (Shi'an Expressway G4), Jingkun Expressway G5, Beijing-Chengde Expressway G45, Changshen Expressway G25, and Zhangjiakou-Chengde Expressway G95, which need good risk prevention measures prepared against the snow disaster.

Key words: snow disaster, traffic, risk assessment, temporal and spatial distribution, Hebei

隋琦, 王瑛, 李婷, 刘庆爱, 俞海洋. 多源信息结合的雪灾交通风险评估研究[J]. 地球信息科学学报, 2018, 20(11): 1571-1578.DOI:10.12082/dqxxkx.2018.180191

SUI Qi,WANG Ying,LI Ting,LIU Qingai,YU Haiyang. Application of Multi-source Information Fusion in the Traffic Risk Assessment of Snow Disaster[J]. Journal of Geo-information Science, 2018, 20(11): 1571-1578.DOI:10.12082/dqxxkx.2018.180191

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参考文献 19

[1]	中国气象局.GX/T 178-2013: 城市雪灾气象等级[S].北京:气象出版社,2013.
	[ China Meteorological Administration.GX/T 178-2013: Meteorological grades of urban snow hazards[S]. Beijing: China Meteorological Press, 2013. ]
[2]	Qiu L, Nixon W A.Effects of adverse weather on traffic crashes: Systematic review and meta-analysis[J]. Transportation Research Record: Journal of the Transportation Research Board, 2008,2055(1):139-146. doi: 10.3141/2055-16
[3]	叶俊明,刘涛,黄勇.冰雪、大雾天气下交通安全保障设施应用研究[J]. 西部交通科技, 2010(11):78-82. doi: 10.3969/j.issn.1673-4874.2010.11.018
	[ Ye J M, Liu T, Huang Y.Research on safety protection facilities under snow and foggy condition[J]. Western China Communications Science & Technology, 2010(11):78-82. ] doi: 10.3969/j.issn.1673-4874.2010.11.018
[4]	Brown B, Baass K.Seasonal variation in frequencies and rates of highway accidents as function of severity[J]. Transportation Research Record Journal of the Transportation Research Board, 1997,1581(1):59-65. doi: 10.3141/1581-08
[5]	Liang W, Kyte M, Kitchener F, et al.Effect of environmental factors on driver speed: A case study[J]. Transportation Research Record, 1998(1):155-161.
[6]	史培军. 中国自然灾害风险地图集[M].北京:科学出版社,2011.
	[ Shi P J.Atlas of natural disaster risk of China[M]. Beijing: Science Press, 2011. ]
[7]	方伟华,王静爱,史培军,等.综合风险防范:数据库、风险地图与网络平台[M].北京:科学出版社,2011.
	[ Fang W H, Wang J A, Shi P J, et al.Comprehensive risk prevention: database, risk map and network platform[M]. Beijing: Science Press, 2011. ]
[8]	Blaikie P, Cannon T, Davis I, et al.At risk: Natural hazards, people's vulnerability and disasters[J]. Economic Geography, 2003,72(4):460-463.
[9]	周艳,李妍羲,江荣贵,等.交通拥堵与预警信息交互传播动力学分析[J].地球信息科学学报,2017,19(10):1279-1286. doi: 10.3724/SP.J.1047.2017.01279
	[ Zhou Y, Li Y X, Jiang R G, et al.Dynamic analysis of interactive transmission of warning information and traffic congestion[J]. Journal of Geo-information Science, 2017,19(10):1279-1286. ] doi: 10.3724/SP.J.1047.2017.01279
[10]	Jiang W G, Deng L, et al.Risk assessment and validation of flood disaster based on fuzzy mathematics[J]. Progress in Natural Science: Materials International, 2009,19(10):1419-1425. doi: 10.1016/j.pnsc.2008.12.010
[11]	尹占娥,许世远,殷杰,等.基于小尺度的城市暴雨内涝灾害情景模拟与风险评估[J].地理学报,2010,65(5):553-562. doi: 10.11821/xb201005005
	[ Yin Zh E, Xu Sh Y, Yin J, et al.Small-scale based scenario modeling and disaster risk assessment of urban rainstorm water-logging[J]. Acta Geographica Sinica, 2010,65(5):553-562. ] doi: 10.11821/xb201005005
[12]	王瑛,林齐根,史培军.中国地质灾害伤亡事件的空间格局及影响因素[J].地理学报,2017,72(5):906-917. doi: 10.11821/dlxb201705011
	[ Wang Y, Lin Q G, Shi P J.Spatial pattern and influencing factors of casualty events caused by landslides[J]. Acta Geographica Sinica, 2017,72(5):906-917. ] doi: 10.11821/dlxb201705011
[13]	宋海权. 基于复杂网络理论的网络交通拥堵问题研究[D].成都:西南交通大学,2016.
	[ Song H Q.Research of network traffic congestion based on complex networks theory[D]. Chengdu: Southwest Jiaotong University, 2016. ]
[14]	Kim W.Development of a hybrid prediction model for freeway incident duration: A case study in Maryland[J]. International Journal of Intelligent Transportation Systems Research, 2012,10(1):22-33. doi: 10.1007/s13177-011-0039-8
[15]	Kaabi A A, Dissanayake D, Bird R.Response time of highway traffic accidents in Abu Dhabi[J]. Journal of the Transportation Research Board, 2012,2278(1):95-103. doi: 10.3141/2278-11
[16]	Li R, Pereira F C, Ben-Akiva M E. Competing risk mixture model and text analysis for sequential incident duration prediction[J]. Transportation Research Part C, 2015,54:74-85. doi: 10.1016/j.trc.2015.03.009
[17]	杨勇. 高速公路道路交通风险防控探析—吉林省高速公路应急管理处置风险防控及实践思考[J]. 道路交通管理, 2017(8):35-37.
	[ Yang Y.Analysis of highway traffic risk prevention: A case of the prevention of emergency management of expressway in Jilin province[J]. Road Transportation Management, 2017(8):35-37. ]
[18]	薛刚. 高原地质及气象环境对公路交通风险的影响研究[D].昆明:昆明理工大学,2017.
	[ Xue G.Research on the effect of plateau geology and meteorological environment on highway traffic risk[D]. Kunming: Kunming University of Science and Technology, 2017. ]
[19]	ESPON. Glossary of terms[ED/OL]. , 2003-12-11.

数据名称	数据来源
1984-2015年地面观测站积雪深度	河北省气象局气象灾害防御中心
河北省高速公路数据	中国军民两用交通详图集（2017年）
河北省高速公路灾情数据	新华网、河北省公安厅交通管理局官网、河北日报、河北新闻网、北京日报

暴露度级别	分级标准	主要道路
4	国庆假期、春节往返均出现拥堵	京沪高速G2青县段、京哈高速G1河北段、京港澳高速G4保定段、大广（京承）高速G45衡水和廊坊段、京藏高速G6怀来段、黄石高速G1811藁城段、保津高速G18廊坊段、石太高速G20井陉段等
3	春节往返高峰出现拥堵	张石高速S010张家口和保定段、黄石高速G1811沧州段、张承高速G95、东吕高速G2516邢台段、青兰高速G22邯郸段等
2	国庆假期出现拥堵	邢临高速S009威县段、廊沧高速、京承高速G45渡口堡段、廊涿高速S24等
1	国庆假期、春节往返均无拥堵	二秦高速张家口段、承围高速S50、京哈高速G1迁西支线、京秦高速G1N迁安支线、青龙县段等

多源信息结合的雪灾交通风险评估研究

Application of Multi-source Information Fusion in the Traffic Risk Assessment of Snow Disaster

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