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基于计算实验的BRT优先信号控制研究
其他题名Signal priority for bus rapid transit based on computational experiments
亢文文
学位类型工学硕士
导师熊刚
2015-05-31
学位授予单位中国科学院大学
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词快速公交 公交信号优先 信号协调 深度学习网络 行驶时间预测 计算实验 Bus Rapid Transit Transit Signal Priority Signal Coordination Deep Learning Network Travel Time Prediction Travel Time Prediction
摘要进入新世纪以来,交通拥堵问题已成为世界各大城市的难题。然而由于传统公交系统效率低、不舒适、延误大,无法满足日益增长的交通出行需求。公交信号优先被认为是一种提高公共交通服务水平的有效手段,但是传统公交系统线路众多且错综复杂,无法直接应用。与此同时,快速公交(Bus Rapid Transit,BRT)具有成本低、运量大、服务好的特点,正在吸引越来越多的研究者和工程人员的兴趣。BRT线路简单、信息化程度高,适合应用公交信号优先。这篇硕士论文主要研究如何为快速公交提供信号优先这一难题。 首先,本文简单介绍了快速公交系统的基本概念,并对公交优先研究做了综述。公交优先方法可以大致划分为三类:主动优先,被动优先和实时优先。本文总结了这三类公交优先方法的特点、实现框架和发展历史。 然后本文提出了一种基于信号协调的被动公交优先方法。该方法要求信号灯具有相同的周期,通过估计相邻信号灯之间的行程时间,计算每个信号灯的信号偏置,从而达到信号协调。这种方法简单且易于实现,在适当的条件下可以为公交车节省大量的行程时间。 接着,本文提出一种基于深度网络的面向公交信号优先的连续行驶时间预测模型。这种深度网络由一个堆叠自编码器和一个逻辑斯蒂回归预测器组成。每当车辆经过检测器时,采集车辆经过速度、当前交通流量、交通密度和信号灯时间作为深度网络的输入,网络的输出即为预测的到达路口的连续行驶时间。实验采集了大量的仿真数据用于训练该深度网络,在测试数据集上的实验结果显示,此模型可以将预测的平均绝对误差降至4.13秒,预测精度达到当前先进水平。 基于以上研究,本文提出一种动态的主动公交信号优先模型,组合使用了三种信号优先动作:绿灯延长、红灯早断和相位插入。根据预测的公交到达时间窗口宽度计算出对应三种优先动作的时间窗口。当公交车经过检测器时,深度网络预测出公交车到达路口的时间,然后由公交信号优先控制器决定是否给予优先以及应该给予何种优先。文章针对广州某段BRT走廊做了计算实验,结果表明此模型平均单次信号优先可以减少公交行程时间34秒,降低公交行程延误10%,同时对非优先车流的负面影响很小。
其他摘要Traffic congestion problem has been boring many cities worldwide since the new century. Somehow the conventional bus system fails to act as the major role in meeting the increasing urban travel demand, because of its deficiency, uncomfortableness and great latency. TSP (Transit Signal Priority) has been recognized to be an effective way to improve the level of service of public transit. However applying TSP to convetioanl bus system is unpractical because of the great number of bus routes and criss-corsses. At the same time, BRT (Bus Rapid Transit), as a new public transit mode, is attracting more and more interests of researchers and engineers. The BRT routes are simple and of high level of informatization. This dissertation focuses on the problem of providing TSP for BRT buses. Firstly the dissertation gives a brief introduction to BRT systems and reviews the PTP (Public Transit Priority) research. PTP methods can be roughly classified into three categories: passive priority, active priority and real-time priority. Characteristics, implementation frameworks and development histories of the three categories of methods are summarized. Then the dissertation proposes a passive PTP method based on signal coordination. This method requires that all signals have the same cycle length. Signal coordination is achieved by estimating the travel times between consecutive signals and calculating proper signal offset for each signal. This method is simple and easy to implement and can result in great travel time saving for buses under proper conditions. Next the dissertation proposes a continuous travel time prediction model towards active TSP system based on a deep network. The deep network is composed of a stacked autoencoder and a logistic regression predictor. The vehicle speed, traffic flow, traffic density and signal time when the vehicle passes the detector are taken as network input and the output gives the predicted continuous travel time to the intersection. Big data (simulated) are applied to train the model. On the testing data set it produces state-of-the-art prediction accuracy with mean absolute error of 4.13 seconds. Based on the research above, it’s ready for the dynamic active TSP model. Within this model, three signal priority action are combinedly used: green extension, red truncation and phase insertion. Action window for each priority action is located within the signal cycle according to the temporal width of predicted bus arrival window. ...
其他标识符201228014628009
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7754
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
亢文文. 基于计算实验的BRT优先信号控制研究[D]. 中国科学院自动化研究所. 中国科学院大学,2015.
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