交通的大数据的维度管理,用于在郊区公路上的短期交通预测
Arash Rasaizadi1, Fateme Hafizi1, Seyedehsan Seyedabrishami2
1Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
Scientific reports
|January 17, 2024
概括
使用大流量数据预测交通状况至关重要. 循环功能显著提高了交通预测模型的准确性和减少了处理时间,长短期内存达到88.09%的准确性.
科学领域:
- 交通工程是交通工程.
- 数据科学是数据科学.
- 机器学习 机器学习
背景情况:
- 智能系统产生大量,高速和多样化的交通数据,称为大流量数据.
- 管理大流量数据的维度对于有效的分析和预测至关重要.
- 之前的研究重点是处理大量的大数据流量.
研究的目的:
- 使用大流量数据预测郊区道路段的交通状况.
- 研究数据维度管理技术对交通状况预测准确性的影响.
- 为了比较不同机器学习模型的性能,用于交通预测.
主要方法:
- 使用主要组件分析 (PCA) 来减少特征和使用遗传算法 (GA) 来选择特征的数据维度管理.
- 使用循环特征来改变其性质的特征的转换.
- 长短期内存 (LSTM),支持矢量机 (SVM) 和随机森林 (RF) 模型的应用用于交通状态预测.
主要成果:
- 使用循环特征提高了交通状况预测准确度,与具有所有初始特征的基本模型相比.
- 长短期记忆模型利用了71个周期性特征,实现了最高的预测准确率88.09%.
- 通过循环特征的特征减少,模型执行时间从456秒 (基本模型) 减少到201秒.
结论:
- 循环特征有效地提高了交通状态预测模型的准确性.
- 该LSTM模型在预测交通状况方面表现出卓越的性能,其特征集减少.
- 优化功能工程和模型选择可以导致更高效,更准确的交通预测系统.
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