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高速公路异常事件影响范围演化分析与预测研究

发布时间:2019-07-09 07:48
【摘要】:高速公路异常事件会对道路的通行产生较大影响,容易引发交通拥堵,并沿事发点上游迅速蔓延,使得道路资源得不到充分利用。因此,尽可能准确地把握事件影响范围及其发展趋势将有助于提高高速公路的管控和服务水平。异常事件影响范围表现为拥堵车流波的传播范围。但目前对异常事件下拥堵车流波部分影响因素的仿真分析仅在单一车型下分析,此外,现有的大多数基于交通波理论建立的事件影响范围预测模型以及异常事件下拥堵状态演化分析方法适用性不强。对此,以高速公路异常事件为研究对象,本文首先对异常事件下拥堵车流波影响因素进行了分析,其次,结合交通流时空关联特性,给出了基于云模型及相似序列搜索的事发点上游流量预测方法,最后,建立了基于交通波模型及MCTM和云模型的事件影响范围预测及演化分析方法。论文主要内容包括:①考虑异常事件影响范围的时空传播特性,基于时空消耗的思想,应用偏微分方法和数值模拟分析了异常事件的时空影响。结合VISSIM仿真和实测数据,并在仿真中同时考虑大车、小型车,定义表征异常事件扩散影响的数据“变点”和“变点区”,并采用单因素置换的分析方法,分析了多种因素对异常事件下拥堵车流波的影响,从而把握异常事件下拥堵的扩散规律。②针对现有事发点上游流量预测方法缺乏考虑交通流时空关联特性的不足,从高速公路交通流的时空关联特性角度出发,同时考虑交通流的不确定性,提出了一种基于云模型及相似序列搜索的事发点上游流量的预测方法,通过实例分析,验证了该方法的有效性。③针对当前以交通波理论为基础建立的异常事件影响范围预测模型适用性不强等问题,考虑不同空间背景下交通流的差异性,通过对高速公路实测数据的统计分析,应用Van Aerde模型建立了研究路段的交通流模型,并提出了异常事件影响范围预测模型,最后通过对典型事件的分析,验证了该模型的可行性。④针对采用阈值划分的确定性、统一性指标难以准确识别拥堵状态的不足,提出了基于MCTM模型及云模型的异常事件下交通拥堵状态演化分析方法,利用基于云模型的交通拥堵状态估计方法实现元胞状态识别,进而应用MCTM模型实现不同时刻拥堵扩散范围的有效估计,并通过实验验证了方法的合理性和有效性。综合上述研究,形成了高速公路异常事件影响范围演化分析与预测方法,实验表明,该方法合理、可行、有效,具有一定的应用价值。
[Abstract]:The abnormal events of expressway will have a great impact on the traffic of the road, which can easily lead to traffic congestion and spread rapidly along the upstream of the accident point, so that the road resources can not be fully utilized. Therefore, grasping the influence range and development trend of the event as accurately as possible will help to improve the control and service level of expressway. The influence range of abnormal events is the propagation range of congested traffic flow wave. However, at present, the simulation analysis of some influencing factors of traffic congestion flow wave under abnormal events is only under a single vehicle type. In addition, most of the existing event influence range prediction models based on traffic wave theory and the evolution analysis methods of congestion state under abnormal events are not applicable. In this paper, the influencing factors of traffic congestion wave under abnormal events are analyzed in this paper. secondly, combined with the temporal and spatial correlation characteristics of traffic flow, the upstream flow prediction method of incident point based on cloud model and similar sequence search is given. finally, the prediction and evolution analysis method of event influence range based on traffic wave model, MCTM and cloud model is established. The main contents of this paper are as follows: (1) considering the space-time propagation characteristics of the influence range of abnormal events, based on the idea of space-time consumption, the partial differential method and numerical simulation are used to analyze the temporal and spatial effects of abnormal events. Combined with VISSIM simulation and measured data, and considering large car and small vehicle at the same time, the data "variable point" and "variable point area" which characterize the influence of abnormal event diffusion are defined, and the influence of many factors on traffic congestion flow wave under abnormal event is analyzed by using single factor replacement analysis method. In order to grasp the diffusion law of congestion under abnormal events. 2 in view of the lack of considering the temporal and spatial correlation characteristics of traffic flow, a prediction method of upstream traffic flow based on cloud model and similar sequence search is proposed, which is based on cloud model and similar sequence search, from the point of view of temporal and spatial correlation characteristics of expressway traffic flow. The effectiveness of the method is verified. 3 in order to solve the problem that the prediction model of the influence range of abnormal events based on traffic wave theory is not applicable, considering the difference of traffic flow in different spatial backgrounds, through the statistical analysis of the measured data of expressway, the traffic flow model of the research section is established by using Van Aerde model, and the prediction model of the influence range of abnormal events is put forward. Finally, the feasibility of the model is verified by the analysis of typical events. 4 in view of the fact that it is difficult to accurately identify the congestion state by using the certainty of threshold division, the evolution analysis method of traffic congestion state under abnormal events based on MCTM model and cloud model is proposed, and the cellular state identification is realized by using the traffic congestion state estimation method based on cloud model. Furthermore, the MCTM model is used to estimate the congestion diffusion range at different times, and the rationality and effectiveness of the method are verified by experiments. Based on the above research, the evolution analysis and prediction method of the influence range of highway abnormal events is formed. The experimental results show that the method is reasonable, feasible and effective, and has certain application value.
【学位授予单位】:重庆大学
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:U491

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