在使用参数优化和自适应模型选择的5G启用智能交通系统中预测交通流量
Hanh Hong-Phuc Vo1, Thuan Minh Nguyen1, Khoi Anh Bui1
1Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了FVMD-WOA-GA,用于更好地预测5G系统中的流量. 混合方法提高了智能运输的准确性和效率.
科学领域:
- 智能运输系统 智能运输系统
- 交通流量预测预测
- 混合机器学习模型混合机器学习模型
背景情况:
- 准确的流量预测对于5G智能交通系统中高效的运输管理至关重要.
- 现有的方法往往难以捕捉复杂的时间依赖性,并有效地优化预测模型.
- 需要集成先进的分解和优化技术来提高预测准确性.
研究的目的:
- 提出和验证一种新的混合方法,FVMD-WOA-GA,以提高交通流预测的准确性.
- 通过系统地分解交通数据和优化模型选择来提高预测模型的性能.
- 证明该方法在减少智能运输系统的预测错误和推断时间方面的有效性.
主要方法:
- 快速变化模式分解 (FVMD) 用于数据分解.
- 鱼优化算法 (WOA) 和遗传算法 (GA) 用于优化预测模型.
- 使用长短期内存 (LSTM),双向LSTM (BiLSTM),门式循环单元 (GRU) 和双向GRU (BiGRU) 作为预测模型.
主要成果:
- 在两个真实世界交通数据集上实现了152.43和7.91的根平均平方误差 (RMSEs).
- 与现有方法相比,预测准确度有3.44%和12.87%的显著改善.
- 验证了该方法在减少推断时间和提高系统适应性的有效性.
结论:
- 混合FVMD-WOA-GA方法显著提高了5G智能交通系统的流量预测准确度.
- 分解,优化和模型选择的系统的多阶段方法导致了卓越的预测性能.
- 拟议的方法为更高效,更适应的交通管理提供了一个有希望的解决方案.
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