本文選題：山區(qū)高速公路 + 危險品運輸��； 參考：《山東科技大學》2017年碩士論文

【摘要】：隨著我國國民經(jīng)濟的快速增長,公路建設里程數(shù)也在不停地增加。較以往相對施工難度大、投資費用高的山區(qū)道路也有較大的發(fā)展,山區(qū)的道路里程和路網(wǎng)密度都有顯著的提升,危險品道路運輸量也快速增長。根據(jù)歷史數(shù)據(jù)顯示,我國近一半的危險品運輸事故發(fā)生在山區(qū)道路。由于事故發(fā)生點地理位置特殊,救援難度大,加之危險品的特殊危害性,所以事故一旦發(fā)生,往往會造成巨大的后果。因此,本文以山區(qū)高速公路危險品運輸為切入點進行研究。首先,通過對比山區(qū)高速公路和平原區(qū)高速公路事故影響因素的不同,得出道路因素是導致山區(qū)高速公路交通事故最主要的因素。在此基礎上,分析了山區(qū)高速公路的特點,闡述了橋梁、隧道、彎道、坡度對交通運輸安全的影響。然后,闡述了山區(qū)高速公路危險品運輸風險預測的現(xiàn)狀及特點,并分別介紹了常用預測模型及其優(yōu)缺點。結(jié)合山區(qū)高速公路影響因素多且不確定的特點,選用預測精度較高的灰色GM (1,1)與BP神經(jīng)網(wǎng)絡組合算法對濟青高速南線(G22東段)高速公路沂源路段進行危險品運輸事故量的預測,充分利用了灰色預測需求樣本數(shù)據(jù)少,神經(jīng)網(wǎng)絡非線性擬合能力強的優(yōu)點。由所得預測值,判斷此路段危險品運輸風險,同時為交通管理部門的管理和規(guī)劃提供數(shù)據(jù)支撐。最后,采用模擬軟件ALOHA,通過對影響因素進行優(yōu)化、篩選,利用軟件針對選取位置濟青高速南線(G22東段)青島方向K219.073樁號處(118°22'E,36°06'N)進行危險品運輸事故的危害等級和危險范圍模擬。為危險品運輸事故救援提供理論支持,為山區(qū)高速公路危險品運輸事故應急響應提供幫助。
[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 擄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.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U492.336.3

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