Shang Qiang, Tang Yingping, Yin Longjiao
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, 255049, China.
Sci Rep. 2024 Nov 2;14(1):26473. doi: 10.1038/s41598-024-77748-1.
Reliable traffic flow data is not only crucial for traffic management and planning, but also the foundation for many intelligent applications. However, the phenomenon of missing traffic flow data often occurs, so we propose an imputation model for missing traffic flow data to overcome the randomness and instability bands of traffic flow. First, k-means clustering is used to classify road segments with traffic flow belonging to the same pattern into a group to utilize the spatial characteristics of roads fully. Then, the LSTM networks optimized with an attention mechanism are used as the base learner to extract the temporal dependence of the traffic flow. Finally, the AdaBoost algorithm is used to integrate all the LSTM-attention networks into a reinforced learner to impute the missing data. To validate the effectiveness of the proposed model, we use the PeMS dataset for validation, we impute the data with missing data rate from 10 to 60% under three missing modes, and we use multiple baseline models for comparison, which confirms that our proposed model improves the stability and accuracy of imputing the missing data of the traffic flow with different scenarios.
可靠的交通流数据不仅对交通管理和规划至关重要，也是许多智能应用的基础。然而，交通流数据缺失现象经常发生，因此我们提出一种针对缺失交通流数据的插补模型，以克服交通流的随机性和不稳定性。首先，使用k均值聚类将交通流属于同一模式的路段分类为一组，以充分利用道路的空间特征。然后，将用注意力机制优化的长短期记忆（LSTM）网络用作基础学习器，以提取交通流的时间依赖性。最后，使用自适应增强（AdaBoost）算法将所有LSTM-注意力网络集成到一个增强学习器中，以插补缺失数据。为了验证所提模型的有效性，我们使用PeMS数据集进行验证，在三种缺失模式下对缺失数据率从10%到60%的数据进行插补，并使用多个基线模型进行比较，这证实了我们所提模型提高了在不同场景下插补交通流缺失数据的稳定性和准确性。
