山区高速公路危险品运输事故预测及危险评估研究

发布时间：2018-06-23 20:54

本文选题：山区高速公路 + 危险品运输　；参考：《山东科技大学》2017年硕士论文

【摘要】：随着我国国民经济的快速增长,公路建设里程数也在不停地增加。较以往相对施工难度大、投资费用高的山区道路也有较大的发展,山区的道路里程和路网密度都有显著的提升,危险品道路运输量也快速增长。根据历史数据显示,我国近一半的危险品运输事故发生在山区道路。由于事故发生点地理位置特殊,救援难度大,加之危险品的特殊危害性,所以事故一旦发生,往往会造成巨大的后果。因此,本文以山区高速公路危险品运输为切入点进行研究。首先,通过对比山区高速公路和平原区高速公路事故影响因素的不同,得出道路因素是导致山区高速公路交通事故最主要的因素。在此基础上,分析了山区高速公路的特点,阐述了桥梁、隧道、弯道、坡度对交通运输安全的影响。然后,阐述了山区高速公路危险品运输风险预测的现状及特点,并分别介绍了常用预测模型及其优缺点。结合山区高速公路影响因素多且不确定的特点,选用预测精度较高的灰色GM (1,1)与BP神经网络组合算法对济青高速南线(G22东段)高速公路沂源路段进行危险品运输事故量的预测,充分利用了灰色预测需求样本数据少,神经网络非线性拟合能力强的优点。由所得预测值,判断此路段危险品运输风险,同时为交通管理部门的管理和规划提供数据支撑。最后,采用模拟软件ALOHA,通过对影响因素进行优化、筛选,利用软件针对选取位置济青高速南线(G22东段)青岛方向K219.073桩号处(118°22'E,36°06'N)进行危险品运输事故的危害等级和危险范围模拟。为危险品运输事故救援提供理论支持,为山区高速公路危险品运输事故应急响应提供帮助。
[Abstract]:With the rapid growth of our national economy, highway construction mileage is also increasing. Compared with the previous construction, the mountain roads with high investment cost also have great development. The road mileage and road network density of mountain areas have been significantly increased, and the volume of dangerous goods road transportation has also increased rapidly. According to historical data, nearly half of dangerous goods transportation accidents in China occur on mountain roads. Because of the special location of the accident location, the difficulty of rescue, and the special harm of dangerous goods, once the accident occurs, it will often cause huge consequences. Therefore, this article takes the mountain highway dangerous goods transportation as the breakthrough point to carry on the research. Firstly, by comparing the influence factors of expressway accidents in mountainous area and plain area, it is concluded that road factor is the most important factor leading to traffic accidents in mountainous expressway. On this basis, the characteristics of highway in mountainous area are analyzed, and the influence of bridge, tunnel, bend and slope on traffic and transportation safety is expounded. Then, the paper expounds the present situation and characteristics of dangerous goods transportation risk prediction in mountain expressway, and introduces the common forecasting models and their advantages and disadvantages respectively. Combined with the characteristics of many and uncertain influencing factors of expressway in mountainous area, the combined algorithm of grey GM (1 + 1) and BP neural network, which has high prediction precision, is used to forecast the traffic accident of dangerous goods in Yiyuan section of Jiqing South Highway (G22 East Section). It makes full use of the advantages of less sample data of grey forecasting and strong nonlinear fitting ability of neural network. According to the predicted value, the risk of dangerous goods transportation in this section is judged, and the data support for the management and planning of traffic management department is provided at the same time. Finally, the simulation software ALOHAA was used to optimize and screen the influencing factors, and the harm grade and dangerous range of dangerous goods transportation accidents were simulated by the software aiming at selecting the position of K219.073 pile (118 掳22) in Qingdao direction of Ji-Qing Expressway South Line (G22 East Section). It provides theoretical support for the rescue of dangerous goods transportation accidents and provides help for the emergency response of dangerous goods transportation accidents on mountain highways.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U492.336.3

【参考文献】