基于关联交叉口交通流量短时预测方法研究
发布时间：2018-11-09 15:10
【摘要】：交叉口作为城市道路网络的节点,在整个城市网络体系中起着至关重要的作用,城市主干道上交叉口往往会成为城市交通活动的瓶颈,一点不畅可能引发整条线,甚至整个路网的连锁反应。对主干道交叉口节点的合理控制可以减少拥堵,降低停车延误,提高通行效率,减少环境污染。而智能交通技术的发展恰是能够为交叉口信号控制提供一个良好的优化方案。车辆动态诱导系统作为智能交通系统的重要组成部分,已成为交通管理部门疏导城市道路交通的有效途径,实时准确的流量预测是实现动态路径诱导的基础和关键。因此,对城市道路交通流量短时预测的研究具有重要意义。传统预测方法一般只根据单交叉口历史数据进行预测,只考虑了时间因素而忽略了空间联系,本文以城市关联交叉口的交通流量预测为研究内容。首先分析和研究了国内外针对交叉口短时流量预测的现状、发展趋势和存在的问题。对交通流预测中涉及到的数据采集、交通流特性、交通预测原理进行简要阐述。然后对灰色预测模型进行研究,针对灰色预测模型的不足进行分析,并进行改进。论文针对传统方法的不足提出将时间和空间因素综合考虑,重点研究上下游交叉口流量路径配比和车辆在路段的行程时间,根据关联交叉口之间的空间地理关系提出在线滚动预测短时流量的方法,该方法不需要将所有交通量数据都予以保存,又保证了预测的连续性,提高预测精度,降低误差离散程度。结合现代交通数据采集技术,本文提出利用次关联交叉口流量统计来预测研究目标的交通流量的构想。并以成都府城大道-益州大道交叉口与府城大道-成汉南路交叉口为例,验证了模型的精度和可行性。
[Abstract]:Intersection, as the node of urban road network, plays a vital role in the whole urban network system. Intersection on the urban main road often becomes the bottleneck of urban traffic activities, which may lead to the whole line. Even the chain reaction of the whole road network. The reasonable control of the main road intersection can reduce the congestion, reduce the delay of stopping, improve the traffic efficiency and reduce the environmental pollution. The development of intelligent transportation technology can provide a good optimization scheme for intersection signal control. As an important part of intelligent transportation system, vehicle dynamic guidance system has become an effective way for traffic management departments to channel urban road traffic. Accurate and real-time flow prediction is the foundation and key to realize dynamic path guidance. Therefore, it is of great significance to study the short-term prediction of urban road traffic flow. The traditional forecasting method is usually based on the historical data of a single intersection, only considering the time factor and neglecting the spatial relationship. This paper takes the traffic flow prediction of the urban intersections as the research content. Firstly, the present situation, development trend and existing problems of short-time flow prediction at intersections are analyzed and studied. The data acquisition, traffic flow characteristics and traffic forecasting principle are briefly described. Then the grey prediction model is studied, and the shortcomings of the grey prediction model are analyzed and improved. Aiming at the shortcomings of the traditional methods, the paper puts forward that considering the time and space factors, the paper focuses on the proportion of the flow paths of the upstream and downstream intersections and the travel time of the vehicles on the road sections. According to the spatial and geographical relationship between intersections, a method of on-line rolling prediction of short time flow is proposed. The method does not need to save all traffic volume data, and ensures the continuity of prediction and improves the accuracy of prediction. Reduce the degree of error dispersion. Combined with modern traffic data acquisition technology, this paper puts forward the idea of forecasting the traffic flow of the research target by using the traffic statistics of sub-related intersections. The accuracy and feasibility of the model are verified by taking the intersection of Chengdu Fucheng Avenue-Yizhou Avenue and Fucheng Avenue-Cheng Hannan Road as an example.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491.23
[Abstract]:Intersection, as the node of urban road network, plays a vital role in the whole urban network system. Intersection on the urban main road often becomes the bottleneck of urban traffic activities, which may lead to the whole line. Even the chain reaction of the whole road network. The reasonable control of the main road intersection can reduce the congestion, reduce the delay of stopping, improve the traffic efficiency and reduce the environmental pollution. The development of intelligent transportation technology can provide a good optimization scheme for intersection signal control. As an important part of intelligent transportation system, vehicle dynamic guidance system has become an effective way for traffic management departments to channel urban road traffic. Accurate and real-time flow prediction is the foundation and key to realize dynamic path guidance. Therefore, it is of great significance to study the short-term prediction of urban road traffic flow. The traditional forecasting method is usually based on the historical data of a single intersection, only considering the time factor and neglecting the spatial relationship. This paper takes the traffic flow prediction of the urban intersections as the research content. Firstly, the present situation, development trend and existing problems of short-time flow prediction at intersections are analyzed and studied. The data acquisition, traffic flow characteristics and traffic forecasting principle are briefly described. Then the grey prediction model is studied, and the shortcomings of the grey prediction model are analyzed and improved. Aiming at the shortcomings of the traditional methods, the paper puts forward that considering the time and space factors, the paper focuses on the proportion of the flow paths of the upstream and downstream intersections and the travel time of the vehicles on the road sections. According to the spatial and geographical relationship between intersections, a method of on-line rolling prediction of short time flow is proposed. The method does not need to save all traffic volume data, and ensures the continuity of prediction and improves the accuracy of prediction. Reduce the degree of error dispersion. Combined with modern traffic data acquisition technology, this paper puts forward the idea of forecasting the traffic flow of the research target by using the traffic statistics of sub-related intersections. The accuracy and feasibility of the model are verified by taking the intersection of Chengdu Fucheng Avenue-Yizhou Avenue and Fucheng Avenue-Cheng Hannan Road as an example.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491.23
【参考文献】
中国期刊全文数据库 前10条
1 王珂;田瑞;王菲菲;;基于灰色遗传支持向量机的短时交通流预测[J];武汉理工大学学报(交通科学与工程版);2014年05期
2 胡鹏;;数据挖掘在交通管理中的应用研究[J];计算机安全;2013年10期
3 郭海锋;方良君;俞立;;基于模糊卡尔曼滤波的短时交通流量预测方法[J];浙江工业大学学报;2013年02期
4 党小超;阎林;;基于多元线性自回归模型的流量预测[J];计算机工程;2012年01期
5 余冰;任莎;欧志梅;何玉花;罗玄;;城市交通拥堵问题研究综述[J];物流技术;2011年11期
6 隋亚刚;郭敏;;北京市道路交通流仿真与预测预报系统的研究及应用[J];道路交通与安全;2010年06期
7 李鸿飞;;城市交通规划中路网布局的合理性分析——以兰州城市交通规划理论与实践为例[J];城市发展研究;2008年S1期
8 姚亚夫;曹锋;;基于ARIMA的交通流量短时预测[J];交通科技与经济;2006年03期
9 徐今强,刘智勇;交通流的时间序列建模及预测[J];五邑大学学报(自然科学版);2004年03期
10 姜桂艳,Q，
本文编号：2320768
本文链接：https://www.wllwen.com/kejilunwen/daoluqiaoliang/2320768.html
