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Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

机译:基于网络分析的城市交通信号系统控制结构优化

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摘要

Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers' experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.
机译:诸如SCOOT和SCATS之类的高级城市交通信号控制系统通常使用多级分层控制机制来协调交通网络。在这种机制中,将从交通信号网络中选择几个关键路口,并将该网络划分为不同的控制子区域。传统上,关键路口的选择和控制子区域的划分是根据动态交通量和路口之间的路段长度来执行的,这在很大程度上取决于交通工程师的经验。但是,它忽略了交通网络拓扑的重要固有特性。在本文中,我们将网络分析方法应用于这两个方面,以优化交通系统的控制结构。首先,将提出改进的C-均值聚类算法,以评估路口在交通网络中的重要性,并进一步基于三个指标而不是仅基于传统方法的交通量来确定关键路口。其次,改进的网络社区发现方法将在交通控制分区划分中提供更合理的证据。最后,为了测试网络分析方法的有效性,将构建一个由区域交通控制系统,微仿真软件和信号控制器硬件组成的硬件在环仿真环境。传统方法和建议方法都将在模拟试验台上实施,以评估交通运营绩效指标,例如行驶时间,停车时间,延误和平均车速。仿真结果表明,所提出的网络分析方法可以有效地提高交通控制系统的运行性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第17期|706919.1-706919.9|共9页
  • 作者单位

    North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.;

    North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.;

    North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.;

    North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.;

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  • 入库时间 2022-08-17 13:54:33

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