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Feedback Gating Control for Network Based on Macroscopic Fundamental Diagram

机译:基于宏观基本图的网络反馈门控控制

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摘要

Empirical data from Yokohama, Japan, showed that a macroscopic fundamental diagram (MFD) of urban traffic provides for different network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. This provides new tools for network congestion control. Based on MFD, this paper proposed a feedback gating control policy which can be used to mitigate network congestion by adjusting signal timings of gating intersections. The objective of the feedback gating control model is to maximize the outflow and distribute the allowed inflows properly according to external demand and capacity of each gating intersection. An example network is used to test the performance of proposed feedback gating control model. Two types of background signalization types for the intersections within the test network, fixed-time and actuated control, are considered. The results of extensive simulation validate that the proposed feedback gating control model can get a Pareto improvement since the performance of both gating intersections and the whole network can be improved significantly especially under heavy demand situations. The inflows and outflows can be improved to a higher level, and the delay and queue length at all gating intersections are decreased dramatically.
机译:来自日本横滨的经验数据表明,城市交通的宏观基本图(MFD)为不同的网络区域提供了网络车辆密度与网络空间平均流量之间的单峰低散度关系。这提供了用于网络拥塞控制的新工具。基于MFD,本文提出了一种反馈门控控制策略,可以通过调整门控交叉口的信号时序来缓解网络拥塞。反馈门控控制模型的目的是根据外部需求和每个门控交叉口的通行能力,最大化流出量并适当地分配允许的流入量。使用示例网络来测试所提出的反馈门控控制模型的性能。考虑了两种类型的测试网络内交叉点的背景信号类型,即固定时间和主动控制。大量仿真结果验证了所提出的反馈门控控制模型可以实现帕累托改进,因为两个门控交叉口和整个网络的性能都可以得到显着改善,尤其是在需求大的情况下。可以将流入和流出提高到一个更高的水平,并且所有门控交叉口的延迟和队列长度都将大大减少。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2016年第5期|3528952.1-3528952.11|共11页
  • 作者单位

    Shanghai Univ, Sch Management, 99 Shangda Rd, Shanghai 200444, Peoples R China;

    Shanghai Univ, Sch Management, 99 Shangda Rd, Shanghai 200444, Peoples R China;

    Tongji Univ, Minist Educ, Key Lab Rd & Traff Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China;

    Wuhan Planning & Design Inst, Wuhan 430010, Peoples R China;

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