With the definition of the freezing process under the influence of the road traffic, an early warning model is built based on the gray correlation method for monitoring the hazard from the freezing processes in the road traffic in the south area. For each process, the four indexes, including the average daily precipitation and air temperature, the overall rainy days and frozen days, contain the metric to quantify its hazard degree. With the analysis of the meteorological factors and the road-blocking disaster of the freezing process in south China during January 21-25, 2016, it is concluded that the extremely high risk area (Grade 5) and the road block area have a very good coincidence relationship, and as the level of the hazard degree rises, the ratio of the hit road block rises accordingly. The relationship is applicable for the state highways, the national highways and the provincial highways, while the peak value of the ratio is at the high hazard degree for the state highways but it is at a lower hazard degree for the provincial highways. These characteristics show that the early warning model proposed in this paper not only gives a good indication, but also discriminates the roads of different grades, to provide a sound reference for the traffic risk control.
YANG Jing
,
LIU Yanxiang
,
GAO Jingjing
,
LI Wanyu
,
HAO Shuhui
,
LI Aixun
,
PAN Jinjun
. A study of early warning technique of hazard degree from freezing processes in southern road traffic[J]. Science & Technology Review, 2019
, 37(20)
: 30
-39
.
DOI: 10.3981/j.issn.1000-7857.2019.20.004
[1] 万素琴, 周月华, 李兰, 等. 低温雨雪冰冻极端气候事件的多指标综合评估技术[J]. 气象, 2008, 34(11):40-46.
[2] 杨贵名, 孔期, 毛冬艳, 等. 2008年初"低温雨雪冰冻" 灾害天气的持续性原因分析[J]. 气象学报, 2008, 66(5):737-750.
[3] 王海军, 覃军, 张峻. 中国南方7省冰冻天气时空分布规律研究[J]. 长江流域资源与环境, 2010, 19(7):839-846.
[4] 赵珊珊, 高歌, 张强. 中国冰冻天气的气候特征[J]. 气象, 2010, 36(4):34-38.
[5] 王遵娅, 赵珊珊, 张强. 我国冰冻日出现的气象条件分析及其判别模型[J]. 高原气象, 2011, 30(1):158-163.
[6] 吴古会, 彭芳, 崔庭. 2011年冬季贵州低温雨雪冰冻天气的成因分析[J]. 气象, 2012, 38(3):291-299.
[7] 宗志平, 马杰, 张恒德, 等. 近几十年来冻雨时空分布特征分析[J]. 气象, 2013, 39(7):813-820.
[8] 毛淑君, 李栋梁. 基于气象要素的我国南方低温雨雪冰冻综合评估[J]. 冰川冻土, 2013, 37(1):14-26.
[9] 李蕊. 路面温度和结冰预报模式研制及应用[D]. 南京:南京信息工程大学, 2010.
[10] 杨亚新, 范德新, 魏昆. 南通市高等级公路冬季路面打滑情况分析[J]. 气象科学, 2002, 22(3):321-327.
[11] 谢静芳, 吕得宝, 王宝书. 高速公路路面摩擦气象指数预报方法[J]. 气象与环境学报, 2006, 22(6):18-21.
[12] 刘梅, 尹东屏, 王清楼, 等. 南京地区冬季路面结冰天气标准及其预测[J]. 气象科学, 2007, 27(3):685-690.
[13] Bouilloud L, Martin E, Habets F. Road surface condition forecasting in France[J]. Journal of Applied Meteorology & Climatology, 2009, 48(12):2513-2527.
[14] Liu T, Pan Q, Sanchez J, et al. Prototype decision support system for black ice detection and road closure control[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(2):91-102.
[15] 李华蓉, 赵一, 潘建平. 山区公路雪灾预警评估模型初探[J]. 城市勘测, 2010(2):116-119.
[16] 刘明明, 潘建平, 杨海明. 山区公路冰雪灾害预警评估模型研究[J]. 公路交通技术, 2011(3):27-30.
[17] 贺芳芳, 邵步粉. 上海地区低温、雨雪、冰冻灾害的风险区划[J]. 气象科学, 2011, 31(1):33-39.
[18] 刘勇洪, 扈海波, 房小怡, 等. 冰雪灾害对北京城市交通影响的预警评估方法[J]. 应用气象学报, 2013, 24(3):373-379.
[19] 张金满, 谭桂容, 李飞. 冰雪天气公路通行条件预警指标[J]. 气象科技, 2016, 44(2):331-335.
[20] Fu L, Thakali L, Kwon T J, et al. A risk-based approach to winter road surface condition classification[J]. Canadian Journal of Civil Engineering, 2017, 44(3):182-191.
[21] Toms B A, Basara J B, Yang H. Usage of existing meteorological data networks for parameterized road ice formation modeling[J]. Journal of Applied Meteorology & Climatology, 2017, 56(7):1959-1976.
[22] 燕科, 董雷宏. 全国公路交通阻断信息数据统计与分析[J]. 公路交通科技, 2009, 26(3):121-125.
[23] 朱求安, 张万昌, 余钧辉. 基于GIS的空间插值方法研究[J]. 江西师范大学学报(自然科学版), 2004, 28(2):183-188.
[24] 王颖, 王晓云, 江志红, 等. 中国低温雨雪冰冻灾害危险性评估与区划[J]. 气象, 2013, 39(5):585-591.
[25] 杨静, 柳艳香, 郜婧婧, 等. 一种影响公路交通的冰冻强度计算方法[J]. 气象科技进展, 2017, 7(1):149-154.
[26] 张继权, 李宁. 主要气象灾害风险评价与管理的数量化方法及其应用[M]. 北京:北京师范大学出版社, 2007:79-81.
[27] 刘伟东, 扈海波, 程丛兰, 等. 灰色关联度方法在大风和暴雨灾害损失评估中的应用[J]. 气象科技, 2007, 35(4):563-566.
[28] 魏海宁. 灰色关联度方法在灾害性天气评估中的应用研究[D]. 南京:南京信息工程大学, 2011.
[29] 江琪, 马学款, 王飞. 2016年1月大气环流和天气分析[J]. 气象, 2016, 42(4):514-520.