Work

Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model

Year: 2025

Type: article

Abstract: Accurate, efficient, and reliable traffic flow prediction is pivotal for highway operation and management. However, traffic flow series present nonlinear, heterogeneous, and stochastic characteristics, posing significant challenges to precise prediction. To address this issue, this paper proposes a novel wavelet-LNN model, integrating the strengths of wavelet decomposition and liquid neural networks (LNNs). Initially, multi-scale wavelet decomposition is applied to the original traffic flow data to yield approximation components and detailed components. Subsequently, each component is trained using the LNN. Ultimately, the predicted results of all components of the LNN models are aggregated to derive the final traffic flow prediction. The experiments conducted on four highway datasets demonstrate that the proposed wavelet-LNN model surpasses SVR, LSSVM, LSTM, TCN, and transformer models in prediction performance across R2, MSE, and MAE metrics. Notably, the wavelet-LNN model features the fewest parameters (<2% of typical deep learning models).

Source: Modelling—International Open Access Journal of Modelling in Engineering Science

DOI: 10.3390/modelling6020039

Authors: Yongjun Wu, Hongyun Kang, Weipin Wang, Shuli Zhao, Xuening He, Jingyao Chen

Institution: Chongqing Jiaotong University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,207