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相关概念视频

Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Root-Locus Method01:19

Root-Locus Method

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A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles.

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可靠的车辆路由问题 使用交通传感器 增强信息

Ahmed Almutairi1, Mahmoud Owais2,3

  • 1Department of Civil and Environmental Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括

本研究引入了一种使用深度学习的新型路由框架,以提高运输网络的可靠性. 它增强了流量预测,并减少了传感器需求,以更好地实时管理流量.

关键词:
深度学习是一种深度学习.随机路由 随机路由是指随机路由.交通流量估计 交通流量估计交通传感器 交通传感器车辆路线优化车辆路线优化

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科学领域:

  • 运输工程 运输工程
  • 网络科学 网络科学
  • 数据科学数据科学数据科学

背景情况:

  • 随机路由面临不确定的旅行时间和有限的传感器数据带来的挑战.
  • 由于动态流量波动,传统的自适应路由可能不足于最佳.

研究的目的:

  • 开发一个新的路由框架,集成交通传感器数据增强和深度学习.
  • 提高运输网络中路线选择和网络可观测性的可靠性.

主要方法:

  • 随机流量分配和多目标路线生成.
  • 最佳的交通传感器位置选择.
  • 基于深度学习的流量估计,使用堆叠的Sparse自动编码器 (SAE).

主要成果:

  • 该框架确定了最小的传感器部署,以准确地估计整个网络的流量.
  • 该SAE模型推断了未观察到的链路流,提高了随机流量条件的可观察性.
  • 数据驱动的方法大大减少了传感器部署的需求,同时保持了高流量预测准确度.

结论:

  • 拟议的框架为实时交通管理提供了一个可扩展和具有成本效益的解决方案.
  • 它解决了有限的传感器可用性和完整的网络可观测性之间的差距.
  • 这提高了在随机运输网络中的车辆路由优化.