一种基于改进的变化模式分解和增强乘客流量的混合整体预测模型
Xiwen Qin1, Chunxiao Leng1, Xiaogang Dong1
1School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究引入了一种新的混合模型,用于准确预测乘客流量,增强智能城市的发展. 该模型结合了变化模式分解 (VMD) 和黄金子优化算法 (GJO) 以获得卓越的预测准确性.
科学领域:
- 智能运输系统 智能运输系统
- 智慧城市发展 智慧城市发展
- 数据科学和分析数据科学和分析
背景情况:
- 准确的客流预测对于智能交通和智能城市倡议至关重要.
- 现有的模型可能无法完全捕捉乘客流量数据的复杂动态.
- 提高预测准确度可以显著改善城市规划和资源管理.
研究的目的:
- 提出一种新的混合客流预测模型.
- 提高乘客流量预测的准确性和可靠性.
- 支持智能交通和智慧城市的发展.
主要方法:
- 一个混合模型,集成金优化算法 (GJO),变化模式分解 (VMD) 和提升算法.
- 使用Sobol序列增强的GJO算法改进了VMD.
- 基于内在模式函数 (IMF) 的子序列组合样本和预测提振.
主要成果:
- 拟议的混合模型在三个风景点数据集中实现了高预测准确性.
- 平均绝对百分比错误 (MAPE) 低至0.0424.
- 装配度始终超过95%,显示出强大的模型性能.
结论:
- 开发的混合模型在客流预测准确度方面取得了显著的进步.
- 该模型为城市规划者,运输当局和旅游经理提供了可靠的数据来源.
- 这项研究通过改进的预测能力,有助于智能城市技术的发展.
相关概念视频
Plane Potential Flows
388
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
Uniform...
388
Multicompartment Models: Overview
143
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
143
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
69
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...
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...
69
Turbulent Flow: Problem Solving
129
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
129
Multi-input and Multi-variable systems
106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
106
Probability Histograms
11.6K
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.
11.6K


