Key result
A proposed traffic forecasting model using low rank dynamic mode decomposition of tensor achieved better forecasting performance than baseline approaches under the impact of missing data.
Why the study?
Traffic forecasting accuracy is impaired by missing data, and standard methods that impute missing values first introduce bias and impede real-time prediction.
A novel tensor-based dynamic mode decomposition model improves traffic forecasting accuracy when dealing with missing data.
Answers are grounded in this paper and cite their sources.
May enhance traffic predictions with incomplete data; leaves open real-world validation before deployment.
Traffic forecasting is an important part in realising intelligent traffic management, which helps traffic controllers and travellers make effective decisions. However, traffic forecasting accuracy is often affected by missing traffic data due to hardware and software failure. Therefore, accurate prediction based on incomplete traffic data is an important problem as well as a challenge. Though many approaches recover the missing values before prediction, the errors from the data‐filling step are likely to cause additional bias to the prediction result. Besides, this tactic is difficult to guarantee the timeliness and may impede real‐time prediction. In this case, a traffic forecasting model is proposed to directly predict traffic data with missing values. This model develops tensor formed dynamic mode decomposition, recording the dynamic information of traffic data into a state transition tensor. In addition, the model takes low rank property of the dynamic tensor and the similarity of temporal variation trend into consideration. In order to verify the effectiveness and the robustness of the proposed model, experiments were performed on two real‐world time series datasets. The results demonstrate that the model achieves better performance on forecasting than other baseline approaches under the impact of missing data.
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Zhang et al. (2022) studied Traffic forecasting with missing data. Low rank dynamic mode decomposition of tensor model vs. Other baseline approaches was evaluated on Forecasting accuracy under the impact of missing data. A proposed traffic forecasting model using low rank dynamic mode decomposition of tensor achieved better forecasting performance than baseline approaches under the impact of missing data.
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