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A simulation-neural network model for traffic control at high-speed signalized intersections.

机译：高速信号交叉口交通控制的仿真神经网络模型。

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摘要

The objective of this study is to develop a simulation-neural network model for the design and evaluation of traffic control at high speed signalized intersections.;At high speed signalized intersections (HSSls), high accident potential exists in the roadway section close to the stop line, called the dilemma zone. Several traffic control devices (TCDs), including advance warning signs, yellow interval, and detector placements, have been implemented to reduce the problem.;In practice, the effectiveness of the traffic control devices is significantly impacted by various roadway and traffic conditions at the intersections. Therefore, the applications of traffic control devices under the various conditions need to be carefully designed and evaluated to ensure the safety of motorists at high speed signalized intersections.;In this study, an analytic model, called HSSI-TCA (High Speed Signalized Intersection-Traffic Control Analysis), is developed for the design and evaluation of traffic control at high speed signalized intersections. The HSSI-TCA model precisely simulates the movement of each vehicle and the effect of each traffic control device in a high speed signalized intersection system, and calculates the probability of being caught in the dilemma zone (PBCDZ), which is used as a primary measure of effectiveness (MOE) in evaluating the traffic control at HSSIs. The microscopic simulation and artificial neural network techniques are applied in the model. Application of the neural network technique makes the model "smart" and more accurate in its simulation and analytic performance. Application of the microscopic simulation technique makes it possible to conduct comprehensive system analysis and experiments, and to determine the most effective traffic control plans for high speed signalized intersections under various conditions.;Two case studies are conducted for testing the model. The results of the case studies indicate that the HSSI-TCA model correctly represents the characteristics of traffic flow in HSSI systems, and the effects of the TCDs on the PBCDZ. Through the case studies, the HSSI-TCA model demonstrates its capability in conducting comprehensive and non-accident-based analysis for the design and evaluation of traffic control at HSSIs.
机译：这项研究的目的是开发一个模拟神经网络模型，用于设计和评估高速信号交叉口的交通控制。在高速信号交叉口（HSSls），靠近停车站的道路段中存在高事故可能性线，称为困境区。为了减少该问题，已经实施了多种交通控制设备（TCD），包括提前警告标志，黄色间隔和检测器放置。；实际上，交通控制设备的有效性在很大程度上受到道路和交通条件的影响。交叉路口。因此，需要仔细设计和评估交通控制设备在各种情况下的应用，以确保高速信号交叉口的驾驶员的安全。在本研究中，一种名为HSSI-TCA（高速信号交叉口-交通控制分析），用于设计和评估高速信号交叉口的交通控制。 HSSI-TCA模型在高速信号交叉路口系统中精确模拟了每辆车的运动和每辆交通控制设备的作用，并计算了陷入困境区（PBCDZ）的可能性，这是主要措施评估HSSI的流量控制的有效性（MOE）。该模型应用了微观仿真和人工神经网络技术。神经网络技术的应用使模型“更智能”，并且在仿真和分析性能方面更加准确。微观仿真技术的应用使得有可能进行全面的系统分析和实验，并为各种条件下的高速信号交叉口确定最有效的交通控制计划。进行了两个案例研究以测试模型。案例研究的结果表明，HSSI-TCA模型正确地代表了HSSI系统中业务流的特征，以及TCD对PBCDZ的影响。通过案例研究，HSSI-TCA模型展示了其在进行基于HSSI的交通控制的设计和评估的全面且基于非事故分析的能力。

著录项

  • 作者

    Huang, Xiao Hui.;

  • 作者单位

    University of Cincinnati.;

  • 授予单位 University of Cincinnati.;
  • 学科 Civil engineering.
  • 学位 Ph.D.
  • 年度 1994
  • 页码 235 p.
  • 总页数 235
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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