增强的多目标图形学习方法,以优化对空间和时间特征的交通速度预测
1PSNA College of Engineering and Technology (PSNACET), Dindigul, Tamil Nadu, India. karthikabm@gmail.com.
Scientific reports
|September 30, 2025
概括
这项研究引入了多目标图形学习 (MOGL) 以准确预测交通速度. MOGL通过改善实时交通管理和减少拥堵来增强智能交通系统.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 机器学习用于时空空间数据分析
- 交通工程和交通管理.
背景情况:
- 交通速度预测 (TSP) 对于智能交通系统 (ITS) 至关重要,它可以实现高效的交通管理和城市移动性.
- 由于时间和空间因素的动态性质,现有的TSP方法面临挑战,导致概括问题和预测不稳定性.
- 道路网络中复杂的时空依赖性为准确的交通速度预测带来了重大障碍.
研究的目的:
- 提出一种新的方法,多目标图形学习 (MOGL),以解决交通速度预测的复杂性.
- 在大型道路网络中提高实时交通速度估计的准确性和可靠性.
- 通过更精确的交通速度预测,提高智能交通系统的性能.
主要方法:
- 开发了一种三相MOGL方法,将自适应图样采集与空间时间图神经网络 (AGS-STGNN) 集成在一起.
- 采用帕雷托高效全球优化 (ParEGO) 在自适应图采样中进行多目标贝叶斯优化,以提取精细的空间和时间特征.
- 采用了增强的注意力门式循环单元 (EAGRU),具有特征融合阶段,用于动态优先确定关键路段和时间间隔.
主要成果:
- MOGL方法在基准数据集 (METR-LA和PeMS-BAY) 上表现出卓越的性能,实现了低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
- 在数据集中实现的MAE值为2.09和2.15,RMSE值为3.29和3.22,MAPE值为3.17和3.21.
- 与DSTMAN相比,在METR-LA数据集上显著降低了RMSE,高达28.9%,优于其他最先进的模型,如STGCN变体.
结论:
- 拟议的MOGL方法在实时和大规模的交通速度预测方面取得了重大进展.
- MOGL有效地捕捉了复杂的时空依赖性,从而提高了预测的准确性和可靠性.
- 该模型能够动态优先考虑影响因素,这有助于其在智能运输系统中的性能提高.
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