基于自监督学习的交通数据补全算法

区域高速公路网收费站数量众多，每日产生海量收费数据，但由于设备、网络等因素，部分站点数据传输存在延迟现象，在此情况下已传输的数据往往不能满足实时流量预测的要求。为了实现实时交通数据补全和动态交通流量预测，文中首先提出了一种基于自监督学习的用于高速公路交通流量数据缺失补全的方法，该方法采用了基于注意力机制的时间序列模型（Seq2Seq-Att）；然后使用自监督学习方式对模型进行训练；最后，以广东省高速公路网的80个收费站为例，验证方法的可靠性。结果表明：文中的数据补全方法能够灵活捕捉交通数据中的缺失情况，并根据数据自身的内在关联性，给出合理的补全值；该方法总体优于其他方法，且在不同缺失率下都有较好表现，总体MAPE约为17.7%、WMAPE为12.8%；在高缺失率情况下，该方法相比于其他补全方法有明显的优势。交通量预测结果表明，使用该方法补全的数据进行交通流预测的预测精度接近使用完整数据的情况。

Traffic Data Imputation Based on Self-Supervised Learning

In the regional highway network, there are numerous toll stations generating massive amounts of data on a daily basis. However, due to equipment and network issues, there may be delays in data transmission for some stations. In such cases, the transmitted data may not be sufficient to meet the requirements for real-time traffic flow prediction. To achieve real-time traffic data imputation and dynamic traffic flow prediction, this paper firstly proposed a method for data imputation of highway traffic flow data based on self-supervised learning, which adopts time series model based on attention mechanism (Seq2Seq-Att). Then the self-supervised learning method was used to train the model. Finally, the reliability of the method was verified by taking 80 toll stations in the highway network of Guangdong province as an example. The results show that the method in this paper can flexibly capture the missing pattern in traffic data and give a reasonable value according to the internal correlation of the data. This method is generally superior to other methods and has good performance under different missing rates. The overall MAPE is about 17.7% and the WMAPE is 12.8%. In the case of high missing rate, this method has obvious advantages over other methods. The results of traffic volume prediction indicate that the prediction accuracy of traffic flow prediction using the data completed by this method is close to the situation of using complete data.