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:在拥挤的区域和高峰期,预测交通流量的能力变得越来越重要,因为它可以帮助当局针对拥堵采取有根据的决策或安排基础设施建设。然而,交通流量预测中的一个关键挑战是每日和每周周期之间时间峰值的缓慢变化,导致交通流量信号的非平稳性,进而难以准确预测。为了解决这个挑战,我们提出了一种慢变化关注的机器学习方法,用于交通流量预测,包括两个部分。首先,我们利用经验模态分解作为特征工程,缓解交通流量数据的非平稳性,产生一系列平稳分量。其次,由于长短时记忆网络在捕捉时间特征方面的优越性,我们将平稳分量作为输入,开发了一种先进的交通流量预测模型。最后,我们将此方法应用于真实数据的基准模型,并与其他现有方法进行比较。我们的方法在均方根误差和平均绝对百分比误差这两个评估指标上的表现比现有技术大幅提高了14.55%和62.56%。