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.
翻译:准确而及时的交通流量预测在开发智能交通系统方面发挥着关键作用,近几十年来吸引了相当的关注。尽管在这一领域取得了深层次学习带来的显著进展,但挑战依然存在。交通流量通常在短期内发生巨大变化,使当前方法无法准确把握未来趋势,并可能导致过大问题,导致不满意的准确性。为此,本文件提议采用基于长期短期内存(LSTM)的方法,能够准确预测短期交通流量,避免培训期间出现当地最佳问题。具体地说,我们不直接使用非固定性原交通数据,而是首先将这些数据分解为次构成部分,其中每个构成部分的频率都比原投入低。之后,样本 Entropy (SE) 被用于合并类似部件,以减少计算成本。合并的功能被输入LSTM,然后我们引入一个极端隐蔽的模块,以考虑在重新混合信号中的相邻关系,以避免强烈的自动调节。在培训期间,我们使用灰色沃尔夫·阿尔戈里特姆(GWO) 来将数据转换成次构成部分,以优化的参数,使其与原投入较不吵不吵的输入。随后的输入,以优化的交通趋势的参数,以最佳的参数,从而克服了拟议的UKSTM系统运行结果,从而显示更好的交通运行结果。