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Applications of ANNs in transportation engineering: Development of a neural traffic signal control system.

机译:人工神经网络在交通运输工程中的应用:神经交通信号控制系统的开发。

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摘要

Artificial neural networks are one of the recently explored advanced technologies which show promise in the area of transportation engineering. However, in contrast to the availability of a large number of successful application demonstrations, it is hard to find studies in the literature that provide systematic examinations of the-state-of-the-art, the application domains, and the applicability of artificial neural networks to transportation problems. On the other hand, some unseen artificial neural networks development has been motivated by transportation engineering objectives. Therefore, this document presents examinations of artificial neural network applications in transportation engineering as well as the development of a neural network paradigm for the purpose of roadway traffic signal control for isolated intersections.; The characteristics, properties, and application domains of artificial neural networks and their relationship to neural science and other artificial intelligence techniques, such as expert systems, are reviewed. The review attempts to project the application domains of artificial neural networks in a general sense. Then, through a literature study, the state-of-the-art in application of artificial neural networks to transportation problems is described. Through examinations of numerous applications, the applicability, justifications, advantages and limitations of artificial neural networks for transportation problems are summarized as well.; The next generation of roadway transportation systems will be formed of so-called intelligent transportation systems. It is anticipated that the software component of such intelligent transportation systems will rely on multiple advanced technologies, including artificial neural networks. This document also provides an assessment of the application potential of artificial neural networks to intelligent transportation systems.; To better demonstrate the use of artificial neural networks in solving different types of transportation problems, a number of real-world applications are presented in detail. These applications, which utilize existing artificial neural network techniques, cover several aspects of transportation engineering, including demand study, facilities maintenance, planning and management.; Artificial neural networks are still under development. New advantages, from which transportation engineering can choose, are expected to be revealed as the development progresses. This dissertation dedicates a large amount of attention to the creation of an operation-time weights changing neural network paradigm for creating a traffic signal control system for an isolated intersection. This system focuses on adaptive, "human-thinking-like," and self-organizing performance. Thorough validation of the control mechanism is included.; Finally, this document summarizes the application domains and applicability, advantages and limitations of artificial neural networks in transportation engineering; presents an evaluation of the neural signal control system; and provides recommendations for future studies in this area.
机译:人工神经网络是最近探索的先进技术之一,在交通工程领域显示出了希望。但是,与大量成功的应用演示相比,在文献中很难找到提供对现有技术,应用领域和人工神经网络适用性的系统检查的研究。网络交通问题。另一方面,运输工程的目标推动了一些看不见的人工神经网络的发展。因此,本文介绍了人工神经网络在交通运输工程中的应用研究,以及为隔离交叉路口的道路交通信号控制而开发的神经网络范例。审查了人工神经网络的特性,属性和应用领域,以及它们与神经科学和其他人工智能技术(例如专家系统)的关系。这篇综述试图从广义上预测人工神经网络的应用领域。然后,通过文献研究,描述了将人工神经网络应用于运输问题的最新技术。通过对众多应用的研究,总结了人工神经网络在运输问题上的适用性,理由,优势和局限性。下一代道路运输系统将由所谓的智能运输系统组成。可以预料,这种智能交通系统的软件组件将依赖于多种先进技术,包括人工神经网络。该文件还提供了对人工神经网络在智能交通系统中的应用潜力的评估。为了更好地说明人工神经网络在解决不同类型的运输问题中的使用,详细介绍了许多实际应用。这些利用现有人工神经网络技术的应用程序涵盖了运输工程的多个方面,包括需求研究,设施维护,规划和管理。人工神经网络仍在开发中。随着开发的进展,运输工程可以从中选择新的优势。本论文致力于大量的工作时间权重变化神经网络范式的创建,以创建一个隔离路口的交通信号控制系统。该系统专注于自适应,“类似于人的思维”和自组织性能。包括对控制机制的全面验证。最后,本文总结了人工神经网络在交通运输工程中的应用领域和适用性,优缺点。介绍了神经信号控制系统的评估;并为该领域的未来研究提供建议。

著录项

  • 作者

    Hua, Jiuyi.;

  • 作者单位

    University of Delaware.;

  • 授予单位 University of Delaware.;
  • 学科 Engineering Civil.; Transportation.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 237 p.
  • 总页数 237
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;综合运输;人工智能理论;
  • 关键词

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