基于改进的深度极端学习机器的流量预测
Xiujuan Tian1, Shuaihu Wu1, Xue Xing2
1School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, 130118, Jilin, China.
Scientific reports
|March 3, 2025
概括
本研究介绍了一种用于短期流量预测的新型混合模型,使用深度极端学习机器与搜索算法 (SSA-DELM) 和自适应分解技术来提高准确性.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 准确的短期交通流量预测对于智能交通系统至关重要.
- 现有的模型经常与交通数据的复杂,非线性动态作斗争.
- 混合模型通过整合多种分析方法来提高预测准确性的潜力.
研究的目的:
- 为短期交通流量提出一种新的混合预测模型.
- 通过结合先进的信号分解和机器学习技术来提高预测准确度.
- 用现实世界的交通数据对现有方法进行模型性能评估.
主要方法:
- 用自适应噪声 (ICEEMDAN) 改进完整集体实证模式分解,用于将信号分解为内在模式函数 (IMF).
- 变 (PE) 分析以描述IMF的随机性.
- 使用Sparrow搜索算法-深度极端学习机器 (SSA-DELM) 进行混合预测,用于高的IMF和ARIMA用于低的IMF.
- 通过汇总各个IMF的预测值来进行合并预测.
主要成果:
- 拟议的SSA-DELM混合模型显示了最小的预测误差.
- 该模型表现出与测量流量数据相匹配的最佳效果.
- 性能评估证实了该模型在提高短期流量预测准确度方面的有效性.
结论:
- 采用ICEEMDAN和PE的混合SSA-DELM模型为短期流量预测提供了卓越的性能.
- 基于随机性特征的分解允许针对不同信号组件进行量身定制的建模.
- 这些发现表明,通过准确的预测来加强交通管理和规划的有希望的方法.
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