  Precise and timely traffic flow prediction plays a critical role in
developing intelligent transportation systems and has attracted considerable
attention in recent decades. Despite the significant progress in this area
brought by deep learning, challenges remain. Traffic flows usually change
dramatically in a short period, which prevents the current methods from
accurately capturing the future trend and likely causes the over-fitting
problem, leading to unsatisfied accuracy. To this end, this paper proposes a
Long Short-Term Memory (LSTM) based method that can forecast the short-term
traffic flow precisely and avoid local optimum problems during training.
Specifically, instead of using the non-stationary raw traffic data directly, we
first decompose them into sub-components, where each one is less noisy than the
original input. Afterward, Sample Entropy (SE) is employed to merge similar
components to reduce the computation cost. The merged features are fed into the
LSTM, and we then introduce a spatiotemporal module to consider the neighboring
relationships in the recombined signals to avoid strong autocorrelation. During
training, we utilize the Grey Wolf Algorithm (GWO) to optimize the parameters
of LSTM, which overcome the overfitting issue. We conduct the experiments on a
UK public highway traffic flow dataset, and the results show that the proposed
method performs favorably against other state-of-the-art methods with better
adaption performance on extreme outliers, delay effects, and trend-changing
responses.
