相关实验视频

Updated: Jul 23, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

在城市铁路运输网络中检测乘客流动异常,使用图形卷积网络告知器和高斯贝斯模型

Bing Liu¹, Xiaolei Ma^1,2, Erlong Tan¹

¹School of Transportation Science and Engineering, Beihang University, Beijing 100191, People's Republic of China.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

|July 16, 2023

概括

本研究引入了一种新的深度学习方法,用于检测城市铁路运输网络中的乘客流异常. 该方法通过分析复杂的空间和时间数据,有效地识别网络层面的干扰.

科学领域:

运输科学运输科学
人工智能的人工智能
网络分析网络分析

背景情况:

检测乘客流动异常对于城市铁路运输网络管理至关重要.
现有的方法往往忽略了乘客流的复杂的空间和时间动态.
需要先进的技术来捕捉网络中的高维乘客行为.

研究的目的:

提出一种新的深度学习框架,用于检测城市轨道交通网络 (URTNs) 的网络级客流异常.
通过结合乘客流的空间和时间特征来解决现有方法的局限性.
提高复杂的运输系统中异常检测的准确性和效率.

主要方法:

一个深度学习框架,结合了图形卷积网络 (GCN) -Informer模型和高斯素朴贝叶斯模型.
该GCN-Informer模型捕捉了乘客流量的时空特征.
高斯的天真贝叶斯模型构建了一个用于异常检测的二进制分类器.

主要成果:

拟议的 GCN-Informer 和 Gaussian Naive Bayes 框架在检测网络级客流异常方面表现出卓越的性能.
在现实世界北京URTN数据集上的实验结果验证了该方法的有效性.
该框架成功地捕捉了复杂的空间和时间客流特征.

结论:

新的深度学习框架为城市铁路运输的乘客流异常检测提供了显著的进步.
与传统方法相比,这种方法可以更全面地了解网络动态.
这些发现有助于改善城市铁路运输网络的运营规划和控制.

关键词:

在GCNinformer模型中.高斯的天真贝叶斯模型.检测异常检测异常检测深度学习模型深度学习模型客流是指旅客流的流量.空间时间的依赖关系.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

相关概念视频

The Power Flow Problem and Solution

The Power Flow Problem and Solution

Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk, phase angle δk, real power Pk, and reactive power Qk. Two of these four variables are inputs, while the...

Fast Decoupled and DC Powerflow

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

Signal Flow Graphs

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...

Probability Histograms

Probability Histograms

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.

Rapidly Varying Flow

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...