VANET环境下基于路径优先级的交叉口自适应控制

发布时间：2018-04-22 14:36

本文选题：车联网 + 自适应控制　；参考：《大连理工大学》2015年硕士论文

【摘要】：随着国民经济的快速发展,城市机动车的数量持续增加。能源消耗与城市交通拥堵、机动车交通安全等问题已成为制约我国交通运输业可持续发展的瓶颈。城市交通的智能化,信息化,一体化成为了解决城市交通拥堵的最关键的技术。但是,传统的交通信号控制系统主要通过埋设静态的传感器或是通过摄像头红外线等手段进行交通流的统计及预测。这不仅仅造成了资源的严重浪费,同时对交通流难以进行精确的衡量,只能是利用历史数据或是依据传感器采集到的单一数据,进行交通流的调控。车联网的出现为彻底的解决城市交通拥堵带来了希望。车联网是将车车、车路之间的通信加入到了城市交通控制系统中,使得行驶的车辆可以与周围的车辆、路边基础设施以及广域网进行无线通信和信息交换,建立高效的交叉口行车控制、车辆安全驾驶和实时信息服务等的一体化网络。但是,由于车联网的发展处于初步阶段,对于大规模的城市交通流的控制疏散的研究较少。因此,本文提出了车联网环境下基于路径优先级的自适应控制。该方法首先设计车联网环境中车路通信的系统框架及结构,然后对整个城市路网进行抽象建模,将城市路网抽象为复杂的有向图。接着利用车联网中的车路通信方式,通过路侧单元收集统计交通流的实时状态信息。路侧单元通过PageRank模型定量的表示出每股交通流的放行优先级。最后,在单个交叉口处通过加权最大团模型,计算出交通流放行的最优相位、相序。使得单次放行的交通流,即保证了高优先级的交通流优先通过,也保证了放行的车流量最大。最后,本文的实验分为两种方式进行。一种方式是在VISSIM仿真平台上进行,主要测试本文提出的自适应控制策略对于车辆调度的效果。另一种方式是真实车辆的现场测试,主要测试系统的功能。结果表明,本文提出的基于路径优先级的自适应控制方法,相比较于传统的自适应控制方法控制效果较好。同时,在现实的真车环境下能够高效的完成交叉口处车辆的调度。
[Abstract]:With the rapid development of the national economy, the number of urban motor vehicles continues to increase. Energy consumption, urban traffic congestion, motor vehicle traffic safety and other problems have become the bottleneck of sustainable development of transportation industry in China. The intelligence, information and integration of urban traffic have become the most critical technology to solve urban traffic congestion. However, the traditional traffic signal control system mainly uses static sensors or infrared camera to calculate and predict traffic flow. This is not only a serious waste of resources, but also difficult to accurately measure traffic flow. It can only use historical data or single data collected by sensors to regulate traffic flow. The emergence of car networking has brought hope for a thorough solution to urban traffic congestion. Vehicle networking is the addition of communication between vehicles and roads to the urban traffic control system so that vehicles can communicate and exchange information wirelessly with surrounding vehicles, roadside infrastructure and wide area networks. An integrated network of efficient intersection traffic control, vehicle safe driving and real-time information service is established. However, due to the initial stage of the development of vehicle networking, there is little research on large-scale urban traffic flow control evacuation. Therefore, an adaptive control based on path priority is proposed in this paper. This method first designs the system framework and structure of vehicle-road communication in the vehicle networking environment, and then abstracts the whole urban road network into a complex directed graph. Then, the real-time state information of traffic flow is collected through road-side unit by means of vehicle-road communication in vehicle networking. The kerbside unit quantificationally represents the priority of traffic flow per share through the PageRank model. Finally, the optimal phase and phase sequence of the traffic flow are calculated by using the weighted maximum cluster model at a single intersection. The single release traffic flow ensures the priority of high priority traffic flow and the maximum traffic flow. Finally, the experiment is divided into two ways. One method is carried out on the VISSIM simulation platform, which mainly tests the effect of the adaptive control strategy proposed in this paper on vehicle scheduling. Another way is the real vehicle field test, the main test system function. The results show that the proposed adaptive control method based on path priority is better than the traditional adaptive control method. At the same time, the vehicle scheduling at the intersection can be completed efficiently in the real vehicle environment.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.54;TN929.5;TP391.44

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