Cai Lingru, Lei Mingqin, Zhang Shuangyi, Yu Yidan, Zhou Teng, Qin Jing
Department of Computer Science, College of Engineering, Shantou University, 515063 Shantou, China.
Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, 999077 Hong Kong, China.
Chaos. 2020 Feb;30(2):023135. doi: 10.1063/1.5120502.
Accurate and timely short-term traffic flow forecasting plays a key role in intelligent transportation systems, especially for prospective traffic control. For the past decade, a series of methods have been developed for short-term traffic flow forecasting. However, due to the intrinsic stochastic and evolutionary trend, accurate forecasting remains challenging. In this paper, we propose a noise-immune long short-term memory (NiLSTM) network for short-term traffic flow forecasting, which embeds a noise-immune loss function deduced by maximum correntropy into the long short-term memory (LSTM) network. Different from the conventional LSTM network equipped with the mean square error loss, the maximum correntropy induced loss is a local similar metric, which is immunized to non-Gaussian noises. Extensive experiments on four benchmark datasets demonstrate the superior performance of our NiLSTM network by comparing it with the frequently used models and state-of-the-art models.
准确及时的短期交通流预测在智能交通系统中起着关键作用，特别是对于前瞻性交通控制而言。在过去十年中，已经开发了一系列用于短期交通流预测的方法。然而，由于内在的随机性和演化趋势，准确的预测仍然具有挑战性。在本文中，我们提出了一种用于短期交通流预测的抗噪声长短期记忆（NiLSTM）网络，该网络将通过最大相关熵推导的抗噪声损失函数嵌入到长短期记忆（LSTM）网络中。与配备均方误差损失的传统LSTM网络不同，最大相关熵诱导损失是一种局部相似性度量，它对非高斯噪声具有免疫力。在四个基准数据集上进行的大量实验通过将我们的NiLSTM网络与常用模型和最新模型进行比较，证明了其优越性能。
