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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

276
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
276
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
40
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

26
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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相关实验视频

Updated: Jun 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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一个基于空间时间图形卷积网络的共享自行车需求预测模型.

Chaoran Zhou1, Jiahao Hu1, Xin Zhang1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括

本研究引入了一个空间时间共享自行车需求预测 (ST-BDP) 模型,以优化自行车放置. 该模型准确预测需求,提高共享自行车效率和城市流动性.

科学领域:

  • 城市规划和交通科学 城市规划和交通科学
  • 数据科学和人工智能数据科学和人工智能
  • 环境可持续性 环境可持续性

背景情况:

  • 基于站点的自行车共享系统往往导致由于不理想的放置策略而导致使用不足的自行车.
  • 现有的模型可能无法充分考虑影响自行车需求的复杂的空间和时间因素.
  • 环保的交通方式,如共享自行车,对于城市拥堵和减少排放至关重要.

研究的目的:

  • 开发一个先进的模型来预测空间用户对站间共享自行车的需求.
  • 提高共享单车系统的效率和资源分配.
  • 为优化共享单车政策和运营提供数据驱动的见解.

主要方法:

  • 介绍了空间时间共享自行车需求预测 (ST-BDP) 模型.
  • 利用包括天气和时间信息在内的多源数据.
  • 使用时空图卷积网络 (STGCN) 与注意力机制和顺序卷积网络.

主要成果:

  • 该ST-BDP模型在真实世界数据集上表现出卓越的性能.
  • 取得了优异的准确度指标:MAE=1.62,MAPE=15.82%,SMAPE=16.14%,RMSE=2.36. 这些指标都非常准确.
  • 在需求预测准确性方面表现优于现有的基线技术.
关键词:
需求图表 需求图表需求预测需要预测.图形卷积网络 (GCN) 是一个图形卷积网络.时间空间的特征.

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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相关实验视频

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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结论:

  • ST-BDP模型在预测自行车共享需求方面取得了重大进展.
  • 准确的需求预测可以指导更有效的共享自行车系统管理和政策制定.
  • 该模型的精度支持改善城市流动性和资源利用在共享运输.