  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.
