The ability to predict traffic flow over time for crowded areas during rush hours is increasingly important as it can help authorities make informed decisions for congestion mitigation or scheduling of infrastructure development in an area. However, a crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles, resulting in the nonstationarity of the traffic flow signal and leading to difficulty in accurate forecasting. To address this challenge, we propose a slow shifting concerned machine learning method for traffic flow forecasting, which includes two parts. First, we take advantage of Empirical Mode Decomposition as the feature engineering to alleviate the nonstationarity of traffic flow data, yielding a series of stationary components. Second, due to the superiority of Long-Short-Term-Memory networks in capturing temporal features, an advanced traffic flow forecasting model is developed by taking the stationary components as inputs. Finally, we apply this method on a benchmark of real-world data and provide a comparison with other existing methods. Our proposed method outperforms the state-of-art results by 14.55% and 62.56% using the metrics of root mean squared error and mean absolute percentage error, respectively.

翻译：高峰时段拥挤区域交通流随时间变化预测能力日益重要，可帮助管理部门制定缓解拥堵的明智决策或规划区域基础设施建设。然而，交通流预测的关键挑战在于日周期与周周期之间时间峰值的缓慢迁移，导致交通流信号呈现非平稳特性，进而影响预测精度。针对该挑战，我们提出一种面向慢变问题的交通流预测机器学习方法，该方法包含两部分：首先，利用经验模态分解作为特征工程技术缓解交通流数据的非平稳性，生成一系列平稳分量；其次，基于长短期记忆网络在时序特征捕捉方面的优势，以平稳分量作为输入构建先进交通流预测模型。最后，我们在真实世界基准数据上应用该方法，并与现有方法进行对比。采用均方根误差和平均绝对百分比误差作为评价指标，所提方法较当前最优结果分别提升14.55%和62.56%。