论文标题
一种基于特征分析的潜在特征分析方法，用于时空流量数据恢复
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery
论文作者
论文摘要
缺少数据是数据驱动的智能运输系统（ITS）中不可避免的常见问题。在过去的十年中，学者们对丢失的流量数据的恢复进行了许多研究，但是如何充分利用时空交通模式以提高恢复性能仍然是一个开放的问题。针对流量速度数据的时空特征，本文将丢失数据的恢复视为矩阵完成问题，并根据隐藏的特征分析提出了一种时空的交通数据完成方法，该方法发现了时空模式，并从不完整的数据中发现了基础结构，以完成回收任务。因此，我们引入了空间和时间相关，以捕获每个维度的主要基础特征。最后，这些潜在特征通过潜在特征分析应用于恢复流量数据。实验和评估结果表明，模型的评估标准值很小，这表明该模型具有更好的性能。结果表明，该模型可以准确估计连续缺少的数据。
Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done many research on the recovery of missing traffic data, however how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open problem. Aiming at the spatio-temporal characteristics of traffic speed data, this paper regards the recovery of missing data as a matrix completion problem, and proposes a spatio-temporal traffic data completion method based on hidden feature analysis, which discovers spatio-temporal patterns and underlying structures from incomplete data to complete the recovery task. Therefore, we introduce spatial and temporal correlation to capture the main underlying features of each dimension. Finally, these latent features are applied to recovery traffic data through latent feature analysis. The experimental and evaluation results show that the evaluation criterion value of the model is small, which indicates that the model has better performance. The results show that the model can accurately estimate the continuous missing data.
