Identification analysis model of traffic accident-prone locations based on geographical view angle
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摘要: 为了对交通事故多发情况进行全面评价并建立预警机制, 考虑城市道路交通系统的地理视角特点, 综合交通事故多发点鉴别分析方法, 研究了事故多发点分析模型。首先明确被分析道路交通系统的内涵和层次结构, 针对交通系统的点、线、面不同层次确立基本评价指标体系, 集成常规统计法、矩阵分析法和改进的质量控制法等构建事故多发点鉴别分析模型, 并研究了参数的选取及分析结果的输出形式, 最后对某地9条道路的事故多发情况进行了分析。分析结果表明: 道路4为事故多发道路, 道路3具有最高的事故次数和当量事故次数, 道路5具有最高的事故率, 经综合评价, 确定事故多发道路为道路3、4、5, 因此, 该模型可以对道路交通事故多发点进行多层面鉴别分析。Abstract: In order to evaluate traffie accident-prone locations thoroughly and establish early- warning system, the geographical view characteristics of urban road traffic system was studied. The analysis model of traffic accident-prone locations was investigated through the application of relevant methods. The meaning and structure of evaluated road traffic system was confirmed. From different layers of traffic system including spot, line and plane, basic evaluation index system was established, and the analysis model of traffic accident-prone locations was constructed by integrating common statistics method, matrix analysis method and improved quality control method. The data selections of important parameters and the output forms of analysis results were discussed, and the accident-prone locations of nine roads in some city were analyzed. Analysis result indicates that road 4 is accident-prone road, road 3 has the most accident numbers and equivalent accident numbers, and road 5 has the most accident rate. After comprehensive evaluation, roads 3, 4, 5 are identified as accident-prone roads. So the model can identify and analyze the road traffic accident-prone locations comprehensively. 3 tabs, 6 figs, 10 refs.
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图 2 基于事故成因分析的数据统计法
Figure 2. Data statistic method based on accident causation analysis
图 5 事故多发点鉴别分析基本流程
Figure 5. Basic identification analysis flow of accident-prone locations
表 1 分析对象及其评价指标
Table 1. Analysis objects and evaluation indexes
表 2 事故数据
Table 2. Accident data
表 3 鉴别分析结果
Table 3. Results of identification analysis
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[1] 裴玉龙. 道路交通事故多发点质量控制鉴别法的改进[J]. 哈尔滨工业大学学报, 2006, 38(1): 97-100. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX200601027.htmPEI Yu-long. Improvement in the quality control method to distinguish the black spot of the road[J]. Journal of Harbin Institute of Technology, 2006, 38(1): 97-100. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX200601027.htm [2] 张景宏. 北京市道路交通事故多发点及多发路段的鉴别与治理[J]. 道路交通与安全, 2002(4): 22-27. https://www.cnki.com.cn/Article/CJFDTOTAL-DLJA200204004.htmZHANG Jing-hong. Identifying and improvement for road traffic accident prone spots and sections of Beijing[J]. Road traffic and safety, 2002(4): 22-27. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLJA200204004.htm [3] 杜心全. 交通事故多发地点鉴别标准与方法研究[J]. 中国人民公安大学学报: 自然科学版, 2002(5): 68-71. https://www.cnki.com.cn/Article/CJFDTOTAL-GOAN200205018.htmDU Xin-quan. Research of identifying standard and method for traffic accident prone locations[J]. Journal of Chinese People's Public Security University: Science and Technology, 2002(5): 68-71. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GOAN200205018.htm [4] 裴玉龙, 戴彤宇. 鉴别道路交通事故多发点的模糊评价法[J]. 公路交通科技, 2005, 22(6): 121-125, 138. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200506033.htmPEI Yu-long, DAI Tong-yu. Fuzzy evaluating method to distinguish black spot[J]. Journal of Highway and Transportation Research and Development, 2005, 22(6): 121-125, 138. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200506033.htm [5] 刘安业, 王要武, 张磊. 基于BP神经网络的道路交通事故多发点鉴别新方法[J]. 土木工程学报, 2008, 41(6): 108-111. doi: 10.3321/j.issn:1000-131X.2008.06.017LIU An-ye, WANG Yao-wu, ZHANG Lei. New method for identifying road traffic accident-prone locations based on BP neural network[J]. China Civil Engineering Journal, 2008, 41(6): 108-111. (in Chinese) doi: 10.3321/j.issn:1000-131X.2008.06.017 [6] 孟祥海, 盛洪飞, 陈天恩. 事故多发点鉴别本质及基于BP神经网络的鉴别方法研究[J]. 公路交通科技, 2008, 25(3): 124-129. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200803026.htmMENG Xiang-hai, SHENG Hong-fei, CHEN Tian-en. Essence and an approach to identify the hazardous locations of urban arterial link based on BP artificial neural network[J]. Journal of Highway and Transportation Research and Development, 2008, 25(3): 124-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200803026.htm [7] SONGCHITRUKSA P, TARKO A P. The extreme value theory approach to safety estimation[J]. Accident Analysis and Prevention, 2006, 38(4): 811-822. [8] 张力. 基于GIS与主成份分析的道路交通事故黑点研究[J]. 地理空间信息, 2008, 6(5): 79-82. https://www.cnki.com.cn/Article/CJFDTOTAL-DXKJ200805031.htmZHANG Li. Road traffic accident black-spots based on GIS and main ingredient analysis[J]. Geospatial information, 2008, 6(5): 79-82. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DXKJ200805031.htm [9] 秦利燕, 邵春福, 缐凯. 基于GIS的道路交通安全管理系统的研究与设计[J]. 交通运输系统工程与信息, 2005, 5(2): 104-107. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT200502018.htmQIN Li-yan, SHAO Chun-fu, XIAN Kai. GIS-based management information system for road traffic safety[J]. Journal of Transportation Systems Engineering and Information Technology, 2005, 5(2): 104-107. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT200502018.htm [10] 赵苑. 基于GIS的城市交通安全预警系统[J]. 中国新技术新产品, 2008(10): 49. https://www.cnki.com.cn/Article/CJFDTOTAL-XPJX200814049.htmZHAO Yuan. City traffic warning system based on GIS[J]. China New Technologies and Products, 2008(10): 49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XPJX200814049.htm -
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