Year: 2024
Type: conference-paper
Abstract: In transportation management, short-term traffic flow prediction plays a crucial role in road planning and system layout. This study proposes a hybrid Hidden State Fusion Long Short-Term Memory (HSFLSTM) model that integrates Empirical Mode Decomposition (EMD) and Mogrifier to predict short-time traffic flow. By decomposing traffic data using EMD, relatively stable sub-sequences are obtained. The Mogrifier is then employed to interact with input and hidden states, enhancing their correlation. The HSFLSTM model combines these two methods for training and predicting short-term traffic flow data. Experimental results demonstrate that on the PeMS dataset, the model achieves a MAPE evaluation metric of 4.839%, significantly outperforming other models.
Authors: Mingjia Yang, Yuelei Xiao
Institution: Xi’an University of Posts and Telecommunications
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411729888