云梧高速公路前三事故黑点辨别与改善方案设计(含CAD图)

云梧高速公路前三事故黑点辨别与改善方案设计(含CAD图)(任务书,开题报告,外文翻译,论文18000字,CAD图纸3张)
摘要
随着我国经济和科学技术的高速发展，我国高速公路总里程数已经进入世界前列，改变了我国公路交通的整体格局，缓解了许多公路交通“瓶颈”问题所带来的压力，给我国经济社会带来了巨大的经济效益，同时也面临着诸如交通事故率上升、事故死亡率增加等负面影响。高速公路的安全性问题已上升为社会性的问题，因此研究道路交通的微观特征与交通事故的关系显得尤为重要。
本文以云梧高速公路为研究实例，首先对云梧高速公路近五年事故资料进行统计分析，得出影响交通事故的最主要因素——驾驶员。然后结合国内外交通安全相关研究成果，运用累计频率曲线法和基于合理分段的事故黑点辨别方法，找出云梧高速公路的16个事故黑点，在此基础之上，选取道路交通事故的影响指标并构建贝叶斯网络模型，通过结构学习的方法获取贝叶斯模型的结构与相应参数，最后预测出每一事故黑点段的事故率，比较16个黑点段的事故率对事故黑点治理紧迫性进行排序，得出前三事故黑点。通过辨识本研究公路前三事故黑点可能存在的交通安全不利因素，并从交通工程和交通管理角度，提出相应的交通安全完善建议，供运营管理单位和设计单位参考，以保障公路交通安全水平和服务水平。

关键词：交通安全；事故黑点；贝叶斯网络；安全改善设计
 
Abstract
With the rapid development of China's economy and science and technology, the total number of highways in China has entered the world's forefront, which has changed the overall pattern of China’s highway traffic and relieved many of the pressures caused by the “bottleneck” problem of highway traffic.It has brought huge economic benefits to China’s economic and society, but also faced such negative effects as increased traffic accident rates and increased accidental death rates. The issue of highway safety has risen to a social problem. Therefore, it is particularly important to study the relationship between road traffic microscopic features and traffic accidents.
This paper takes the Yun-Wu Expressway as an example. First of all, it analyzes the accident data of the Yun-Feng Freeway in the past five years and draws the conclusion that the driver is the most important factor influencing the traffic accident. Then, combining domestic and international traffic safety related research results, using the cumulative frequency curve method and the method of distinguishing accident black spots based on reasonable segmentation, we identified 17 accident black spots on Yun-WuExpressway, and based on this, selected road traffic accidents. The impact indicators are constructed and the Bayesian network model is constructed. The Bayesian model structure and corresponding parameters are obtained through the structure learning method. Finally, the accident rate of each black spot segment is predicted, and the accident rate of 17 black spot segments is compared. The urgency of black spot management was ranked and the black spots of the first three accidents were drawn. By identifying the potential traffic safety unfavorable factors in the first three accident black spots of this road study, and from the point of view of traffic engineering and traffic management, put forward corresponding recommendations for traffic safety improvement for the reference of operation management units and design agencies to ensure road traffic safety level And service level. [资料来源：http://THINK58.com]
Key words:Traffic Safety; Black Spots; Bayesian Networks; Safety Improvement Design
 

 目录   
第1章绪论    1
1.1 研究背景    1
1.2 目的和意义    1
1.3 国内外研究现状    1
1.3.1 国外研究现状    1
1.3.2 国内研究现状    2

1.4 研究内容    3
1.5 技术路线    4
第2章事故统计分析    5
2.1 道路交通事故现状    5
2.1.1 事故时间分布    5
2.1.2 事故形态分布    6
2.1.3 事故原因分布    7
2.1.4 天气分布    7
2.2 道路交通事故基本原因分析    8
2.2.1 道路条件因素    8
2.2.2 交通安全设施因素    8
2.2.3 沿线环境因素    8
2.2.4 交通流因素    8
2.2.5 主观因素    9
2.3 本章小结    9
第3章事故黑点辨别    10
3.1 交通事故黑点概念    10
3.2 交通事故黑点鉴别    10
3.2.1 累计频率曲线法及鉴定结果    10
3.2.2 基于合理分段的概率预测法    14
3.2.3 事故黑点的最终确定    15
3.3 本章小结    16
第4章基于贝叶斯网的事故黑点预测    17
4.1 贝叶斯网模型建立    17
4.1.1 贝叶斯网模型节点确定    17
4.1.2 贝叶斯网模型结构学习    17
4.1.3 贝叶斯网模型参数学习    17
4.1.4 贝叶斯网模型概率推理    18
4.2 贝叶斯网模型简介    18
4.3 云梧高速公路事故黑点预测    20
4.4 本章小结    24
第5章事故黑点安全改善    25
5.1 交通安全改进思路    25
5.2 事故黑点具体改进措施    26
5.3 本章小结    29
第6章结论与展望    30
6.1 主要研究结论    30
6.2研究展望    30
参考文献    31
致谢    33