LSTM-based traffic flow prediction with missing data
LSTM-based traffic flow prediction with missing data
复制标题
基于 LSTM 的缺失数据交通流预测
DOI：
10.1016/j.neucom.2018.08.067
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发表时间：
2018-11-27
期刊：
影响因子：
6
通讯作者：
Yang, Bailin
中科院分区：
文献类型：
--
作者：
Tian, Yan;Zhang, Kaili;Yang, Bailin
交通流预测在智能交通系统中起着至关重要的作用。然而，由于交通传感器通常是人工控制的，具有可变长度、不规则采样和缺失数据的交通流数据很难被有效地利用。针对这一问题，本文提出了一种基于长短期记忆(LSTM)的新方法。此外，我们还使用多尺度时间平滑来推断丢失的数据，并通过该方法学习预测残差。我们在CARTRANS性能测量系统(PeMS)数据集和我们自己的交通流数据集上演示了我们的方法的性能。实验结果表明，与其他方法相比，该方法具有更高的交通流量预测精度。(C)2018爱思唯尔B.V.保留所有权利。
Traffic flow prediction plays a key role in intelligent transportation systems. However, since traffic sensors are typically manually controlled, traffic flow data with varying length, irregular sampling and missing data are difficult to exploit effectively. To overcome this problem, we propose a novel approach that is based on Long Short-Term Memory (LSTM) in this paper. In addition, the multiscale temporal smoothing is employed to infer lost data and the prediction residual is learned by our approach. We demonstrate the performance of our approach on both the Caltrans Performance Measurement System (PeMS) data set and our own traffic flow data set. According to the experimental results, our approach obtains higher accuracy in traffic flow prediction compared with other approaches. (C) 2018 Elsevier B.V. All rights reserved.