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首页> 外文学位 >An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO).
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An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO).

机译：使用粒子群优化（PSO）的局部模糊逻辑斜坡计量算法的自动调整策略。

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摘要

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment.;The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks.;The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.
机译：高速公路系统每天变得越来越拥挤。对高速公路交通拥堵的一个贡献是将成排的匝道交通合并到高速公路干线上。作为解决该问题的一种相对低成本的措施，在佛罗里达州迈阿密戴德县I-95州长11英里的两个方向都部署了坡度表。已选择部署在华盛顿州西雅图市实施的本地模糊逻辑（FL）斜坡计量算法。; FL斜坡计量算法由模糊逻辑控制器（FLC）驱动。 FLC取决于一系列可显着改变控制器性能的参数，从而影响斜坡仪表的性能。但是，这些参数的最合适值通常难以确定，因为它们会随着当前交通状况而变化。因此，为了获得最佳性能，必须对参数值进行微调。这项研究提出了一种使用粒子群优化（PSO）精调FLC参数的新方法。 PSO尝试优化FLC的几个重要参数。优化模型的目标函数结合了METANET宏观交通流模型，以最大程度地减少延迟时间，但要考虑到合理的斜坡计量率和FLC参数范围。为了进一步提高性能，并结合了使用离散卡尔曼滤波器的短期交通量预测模块，以预测下游高速公路干线的占用率。这有助于检测下游瓶颈的存在。选择CORSIM微观仿真模型作为评估所提出的PSO调整策略性能的平台。使用CORSIM的运行时扩展（RTE）实施了带有调整策略的斜坡计量算法，并在上述I-95走廊上进行了测试。将具有PSO调整功能的FLC的性能与不具有PSO调整功能的现有FLC的性能进行了比较。结果表明，在节省总行驶时间，平均速度和系统范围的吞吐量方面，具有PSO调整功能的FLC优于现有的FL计量，定时计量和现有条件，而无需进行计量。

著录项

  • 作者

    Zhu, Peng.;

  • 作者单位

    Florida International University.;

  • 授予单位 Florida International University.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 158 p.
  • 总页数 158
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;
  • 关键词

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