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为了解决过饱和状态下短连线的信号交叉口路段长度对延误影响的问题,推导出了基于短连线的过饱和信号交叉口最大延误模型。首先分析经典延误模型的盲区:在连接短连线信号交叉口车辆排队长度达到路段长度后,排队车辆就不能再增加;然后利用排队长度与延误关系推导出适用于该状态下的延误模型,并且提出当短连线相连2个交叉口信号相位差为0时所计算出来的延误是最大延误;最后通过算例对比了提出的延误模型与定数理论延误模型。结果表明:路段长度限制对于过饱和状态下连接短连线的信号交叉口延误的计算有很大影响,所提出的方法能够有效解决这一问题。 相似文献
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信号控制交叉口过饱和交通状态的准确识别是进行控制策略调整及优化的前提和基础,文中对其研究进行综述分析。在文献研究的基础上,给出了信号控制交叉口过饱和状态研究的总体脉络,将研究分为过饱和状态界定和过饱和状态辨识2个主要部分。在过饱和状态界定方面,从孤立交叉口过饱和状态与交叉口群过饱和状态两个方面人手综述了过饱和状态界定的条件和方法。在过饱和状态识别方面,基于本交叉口交通流检测数据的过饱和状态识别基础模型和考虑关联交叉口状态数据及信号控制数据的修正模型2方面综述了信号控制交叉口过饱和状态的识别方法。结合最新研究成果对未来发展方向进行了分析。 相似文献
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城市区域交通具有非线性、动态时变性、不确定性的特点,难以建立精确的数学模型.据此,针对小区域交叉口群过饱和状态,研究了基于模糊控制的信号协调优化方案.将现状交通控制下的交叉口群进口道最大排队长度和平均延误作为模糊控制的输入变量,将交叉口绿灯时间调整量作为输出变量,利用模糊C均值聚类获得输入变量的模糊集合和隶属度函数,通过一级模糊控制器和二级模糊控制器分别对区域交叉口群信号进行协调控制,达到减少区域最大排队长度和平均行车延误的目的.通过对武汉徐东商圈过饱和交通状态下的交叉口群进行多次协调控制,并对现状信号方案和协调信号方案进行Vissim微观仿真,交叉口群最大排队长度平均值由201 m减少为63 .6 m ,平均行车延误由110 .62 s减少为22 .68 s . 相似文献
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基于遗传算法的城市交叉口实时信号控制研究 总被引:8,自引:1,他引:8
研究了相对固定周期条件下交叉口信号配时随路上交通流量变化而实时调整的优化算法。以各相各进口道上总的延误时间最少为目标建立优化函数,以饱和度限制及最小绿灯时间要求为约束条件建立非线性规划模型,采用遗传算法对模型进行求解以得到最优配时方案。通过仿真实例说明了函数构造和求解过程。在仿真实例中比较了不考虑饱和度限制下的优化情况和仅以各相关键进口道的总延误最小为目标的优化情况。结果表明,为保证优化的正确性及合理性,应考虑最大饱和度的限制,并以各相各进口道的总延误最小为目标,建立目标优化函数。 相似文献
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Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network. 相似文献
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Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods. 相似文献
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介绍了计算机网络控制技术和通信技术在汽车生产中应用的基本原理。对比传统的汽车技术,论述了网络控制技术和通信技术强大的优越性。 相似文献
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Learning-based traffic control algorithms have recently been explored as an alternative to existing traffic control logics. The reinforcement learning (RL) algorithm is being spotlighted in the field of adaptive traffic signal control. However, no report has described the implementation of an RL-based algorithm in an actual intersection. Most previous RL studies adopted conventional traffic parameters, such as delays and queue lengths to represent a traffic state, which cannot be exactly measured on-site in real time. Furthermore, the traffic parameters cannot fully account for the complexity of an actual traffic state. The present study suggests a novel artificial intelligence that uses only video images of an intersection to represent its traffic state rather than using handcrafted features. In simulation experiments using a real intersection, consecutive aerial video frames fully addressed the traffic state of an independent four-legged intersection, and an image-based RL model outperformed both the actual operation of fixed signals and a fully actuated operation. 相似文献
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ABSTRACTIn this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network. 相似文献
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孤立交叉口多相位自适应模糊控制及其神经网络实现 总被引:8,自引:2,他引:8
针对城市中心区交叉口交通流分布的特点,综合考虑本相位和相邻相位车道上的车辆排队长度(以下简称“队长”),应用模糊控制和神经网络具有的学习功能,提出了一种孤立交叉口多相位自适应模糊控制算法,该算法采用两个规则前件进行模糊推理,并给出了基于3层神经网络实现的模糊控制器的网络结构及其改进的BP网络训练算法和运行程序,结合已有类似研究成果进行了仿真比较研究,结果表明:该控制方法在信号周期自动调节和减少车辆延误方面都有明显改进,在实现城市交叉口智能控制中具有推广应用价值。 相似文献
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续进式绿波协调和同步式绿波协调,均为经典干线协调控制策略。我国传统使用的续进式绿波协调策略,已无法满足高饱和度交通流的需求,甚至会对复杂多变的交通状况产生负面影响。续进式绿波控制在过饱和状态的失效机理,是由于续进式协调控制中上游交叉口先放行,当下游交叉口排队未完全消散时,导致上游绿灯初期放行车辆的二次停车,造成延误增大甚至排队溢出。过饱和状态时,同步式绿波协调更有利于车辆连续不间断地通过交叉口群,车速越高则绿波带越宽,控制效果越显著。以此为基础,基于 Vissim 仿真的建模分析,对比分析了不同交通饱和度,尤其是过饱和状态下,2种干线协调控制策略效果,并对结论进行了实例验证。 相似文献
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桥梁施工进度控制GERT网络仿真研究 总被引:3,自引:1,他引:3
根据GERT网络特点,引入计算机仿真技术,研究了桥梁施工进度控制模拟仿真网络模型GERTS,通过网络要素的节点和活动仿真,提出了产生规定分布随机数的中值平方法、中值乘法、线性同余法,建立了GERTS计算机模拟网络模型,结合实桥钻孔桩施工网络仿真,编制了仿真网络的计算机程序并进行了模拟分析,其仿真工期都在150d上下波动,随着程序运行次数的增多,该结果越来越接近工期理论计算值150d,仿真效果符合工程实际规律,结果表明:GERT仿真网络是控制桥梁施工进度的一种有效方法。 相似文献