计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 72-80.doi: 10.11896/jsjkx.230100045
黄坤, 孙未未
HUANG Kun, SUN Weiwei
摘要: 交通速度预测是智能交通系统的基础,可以缓解交通拥堵,节约公共资源,提高人们的生活质量。在真实情况下,采集到的交通速度数据通常存在缺失,而现有研究成果大多数只考虑了数据相对完整的场景。文章主要针对缺失场景下的交通速度数据进行研究,捕捉其中的时空相关性,并对未来交通速度进行预测。为了充分利用到交通数据的时空特征,提出了一种新的基于深度学习的交通速度预测模型。首先,提出了“还原-预测”算法,先使用自监督学习方法让模型还原缺失数据,再对交通速度进行预测;其次,引入了对比学习的方法,使得速度时间序列的特征表示更鲁棒;最后,模拟了不同数据缺失率的场景,通过实验验证了所提方法在各种缺失率下的预测准确率都优于现有方法,并设计了实验对对比学习方法和不同的还原算法进行分析,证明了所提方法的有效性。
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