Mixture correntropy with variable center LSTM network for traffic flow forecasting

Abstract

Timely and accurate traffic flow prediction is the core of an intelligent transportation system. Canonical long short-term memory (LSTM) networks are guided by the mean square error (MSE) criterion, so it can handle Gaussian noise in traffic flow effectively. The MSE criterion is a global measure of the total error between the predictions and the ground truth. When the errors between the predictions and the ground truth are independent and identically Gaussian distributed, the MSE-guided LSTM networks work well. However, traffic flow is often impacted by non-Gaussian noise, and can no longer maintain an identical Gaussian distribution. Then, a \begin{document}$ {\overline{\delta }}_{relax} $\end{document}-LSTM network guided by mixed correlation entropy and variable center (MCVC) criterion is proposed to simultaneously respond to both Gaussian and non-Gaussian distributions. The abundant experiments on four benchmark datasets of traffic flow show that the \begin{document}$ {\overline{\delta }}_{relax} $\end{document}-LSTM network obtained more accurate prediction results than state-of-the-art models.

Graphical abstract

Keywords

Traffic flow theory / Machine learning / Robust modeling / Mixture correntropy