公路隧道交通安全状态特征选择与评估方法研究

发布时间：2018-01-17 19:02

本文关键词：公路隧道交通安全状态特征选择与评估方法研究　出处：《福州大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着公路建设事业的快速发展,交通安全隐患逐渐成为社会关注的重点对象之一。公路隧道作为路段的重要构造物,若出现交通事故,会影响车辆的正常行驶甚至导致整个路网的瘫痪,因此,保障公路隧道的运营安全,对维持社会、经济的稳定发展都极其重要。鉴于交通安全的日趋重要性以及公路隧道监控系统的日趋完善,若能通过智能控制的方法,建立一个公路隧道的安全评价体系,对公路隧道的交通安全性能进行评价,对于减少工作人员的工作量和工作难度和方便驾驶员及时获取隧道的安全状况,都有很重要的现实意义。本文围绕公路隧道交通安全状态的特征选择和评估方法进行研究。首先,对尚不成熟的交通安全状态的概念进行定义的解释与特点分析,并说明其在公路隧道交通安全评价中的应用；接着,介绍公路隧道事故特性和危害,以及目前常用的隧道交通安全评价指标,根据已有的道路评价方法的分类,确定公路隧道交通安全状态的分类标准。在对公路隧道交通安全特性进行分析的基础上,研究公路隧道交通安全状态的特征参数。首先,将隧道事故交通流与vissim事故仿真的交通流结果进行比较,确定使用vissim交通仿真模拟隧道的事故状态的可行性,解决隧道事故的交通流数据获取困难的问题,为后续的事故研究提供数据基础；接着,对隧道事故的交通流进行分析,确定了能用微观交通流参数反映隧道环境变化的事实,从而确定公路隧道交通安全状态的特征参数。在确定特征参数的基础上,对公路隧道交通安全状态的评估方法进行研究。本文分别建立了以模糊C均值聚类算法为基础的评估模型和以神经网络模式识别方法为基础的评估模型,分析这两个模型对实际隧道交通安全状态的评估结果。结果显示,以模糊C均值聚类算法建立的评估模型有自动处理数据的优点,然而该方法需要以大量的数据为基础,并且容易陷入局部最优解；以神经网络模式识别方法建立的评估模型,充分发挥人的主观能动性,使评估结果准确性更高,但相较于上一个方法则缺少自动分类的便利。
[Abstract]:With the rapid development of highway construction, traffic safety hidden danger has gradually become one of the key objects of social concern. Highway tunnel as an important structure of road section, if there are traffic accidents. Will affect the normal driving of vehicles and even lead to the paralysis of the entire road network, therefore, to ensure the safety of the operation of road tunnels, to maintain the society. The steady development of economy is extremely important. In view of the increasing importance of traffic safety and the improvement of highway tunnel monitoring system, if we can establish a highway tunnel safety evaluation system through the method of intelligent control. To evaluate the traffic safety performance of the highway tunnel, it can reduce the workload and difficulty of the staff and facilitate the drivers to obtain the tunnel safety condition in time. This paper focuses on the characteristics selection and evaluation methods of road tunnel traffic safety status. First of all. The definition and characteristics of the immature concept of traffic safety state are explained, and its application in highway tunnel traffic safety evaluation is explained. Then, it introduces the characteristics and hazards of highway tunnel accidents, as well as the current commonly used tunnel traffic safety evaluation indicators, according to the existing road evaluation methods classification. On the basis of analyzing the traffic safety characteristics of highway tunnel, the characteristic parameters of road tunnel traffic safety state are studied. Comparing the traffic flow of tunnel accident with that of vissim accident simulation, the feasibility of using vissim traffic simulation to simulate tunnel accident state is determined. To solve the difficult problem of obtaining traffic flow data of tunnel accident, and provide the data basis for the subsequent accident research; Then, the traffic flow of tunnel accident is analyzed, and the fact that the microscopic traffic flow parameters can reflect the change of tunnel environment is determined. In order to determine the road tunnel traffic safety state of the characteristic parameters, on the basis of determining the characteristics of the parameters. In this paper, the evaluation model based on fuzzy C-means clustering algorithm and the evaluation model based on neural network pattern recognition method are established, respectively. The results show that the evaluation model based on fuzzy C-means clustering algorithm has the advantage of automatically processing the data. However, this method needs to be based on a large amount of data, and it is easy to fall into the local optimal solution. The evaluation model established by the neural network pattern recognition method can give full play to the subjective initiative of human beings and make the evaluation results more accurate. However, compared with the previous method, the evaluation model lacks the convenience of automatic classification.
【学位授予单位】：福州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U458

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