基于HALRTC理论的短时交通流预测算法

针对目前短时交通流预测算法多考虑交通流的低维信息特征，导致无法满足预测精准度要求等问题，引入高精度低秩张量填充理论（HALRTC），构建基于周、天、时段等多时间维度的动态张量模型，设计了一种融合高维交通流特征的短时交通流预测算法，并以京港澳高速公路杜家坎路段交通流速度数据为例进行实证验证。研究结果显示，算法能够基于较少历史数据较快达到良好预测效果，可有效实现针对工作日与非工作日的交通流预测，平均绝对误差（MAE）平均值约为3.6%，并能及时跟踪交通流波动性。在缺失数据情况下，所提出算法预测精度随数据缺失比例增大而降低，但相较于3种经典预测算法可表现出更好的预测精度。

Short-term traffic flow prediction algorithm based on HALRTC theory

Considering the present studies on short-term traffic flow prediction algorithms focused on the low dimension information features of traffic flow, which fails to meet the requirements of prediction accuracy, this paper introduces the high accuracy low-rank tensor completion theory to construct dynamic tensor models based on week, day, and period and designs a short-term traffic flow prediction algorithm, which combines the multi-dimensional temporal characteristics of traffic flow. The proposed prediction algorithm is verified using velocity data of the Dujiakan section of Beijing-Hong Kong-Macao Expressway. The results show that this algorithm can achieve good prediction results quickly based on fewer historical data. It can achieve effective prediction for the traffic flow of weekday and weekend, and the mean value of mean absolute error is approximately 3.6%; furthermore, the fluctuation of traffic flow is tracked in real time. For the flow with missing data, the prediction accuracy of the proposed algorithm would decrease with the increase in the ratio of missing data. However, compared with the three classical prediction algorithms, the proposed algorithm shows better prediction accuracy using the preprocessed missing data.