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相关概念视频

Errors in Global Positioning System01:26

Errors in Global Positioning System

38
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,...
38
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

50
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...
50
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

46
The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
46
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

24
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
24
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

38
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...
38
Probability Histograms01:17

Probability Histograms

11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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相关实验视频

Updated: Jun 9, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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用GPS传感器数据预测出租车需求的分布式VMD-BiLSTM模型.

Hasan A H Naji1, Qingji Xue1, Tianfeng Li1

  • 1School of Digital Media, Nanyang Institute of Technology, Chang Jiang Road No. 80, Nanyang 473004, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括

使用混合深度学习模型预测出租车需求可以减少浪费资源和排放. 这种方法有效地处理大规模的轨迹数据,改善出租车服务.

科学领域:

  • 运输科学 运输科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 出租车对于公共交通至关重要,但空置的车辆意味着大量的资源浪费.
  • 短期出租车需求预测平衡了供需,减少了燃料消耗.
  • 现有的机器学习和深度学习模型在计算成本和大规模轨迹数据方面扎.

研究的目的:

  • 开发一种高效的混合深度学习模型,用于准确的出租车需求预测.
  • 用大规模传感器数据解决现有模型的计算挑战.
  • 提高出租车运营效率和利能力.

主要方法:

  • 这是一个混合模型,结合了变化模式分解 (VMD) 和双向长短期记忆 (BiLSTM).
  • VMD将交通特征分解为特定频率的子模式.
  • BiLSTM使用分解功能预测需求,优化了Spark分布式平台的性能.

主要成果:

  • 与最先进的方法相比,拟议的VMD-BiLSTM模型表现出卓越的准确性和效率.
  • 该模型有效地利用大规模的出租车轨迹数据.
  • 在现实数据集上验证了性能,显示了分布式计算的显著改进.
关键词:
GPS 传感器数据数据的数据.双向长期短期记忆 双向长期短期记忆在火花平台的火花平台上.预测出租车需求的预测变化模式分解的变化模式分解

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结论:

  • 混合VMD-BiLSTM模型为出租车需求预测提供了有效的解决方案.
  • 这种方法提高了乘客搜索效率,提高了出租车的利能力.
  • 该模型的分布式性能使其适用于大型运输网络.