Work

Graph Wavelet Neural Controlled Differential Equations Method for Speed Prediction Under Traffic Accidents

Year: 2025

Type: article

Abstract: ABSTRACT Accurate speed prediction is a crucial component of intelligent transportation systems, as it enhances traffic management and operational efficiency. While the majority of existing research concentrates on speed prediction under normal traffic conditions, the occurrence of traffic accidents significantly disrupts typical urban traffic patterns, leading to reduced predictive accuracy. Considering that the disruption caused by accidents is localized and severe, and that the dynamic behavior of traffic flow can be effectively modeled through differential equations, we propose a novel traffic speed prediction model, graph wavelet neural controlled differential equations (GW‐NCDE). The GW‐NCDE model leverages graph wavelet transforms to effectively capture the spatial characteristics of the road network under accident conditions and employs a dual‐layer neural controlled differential equation structure for enhanced predictive performance. Experiments conducted on a real‐world dataset from Wangjing, Beijing, demonstrate that our model outperforms several existing benchmark methods. Particularly in accident scenarios, compared to the best‐performing benchmark, the short‐term prediction error of our model is reduced by more than 10%. These results underscore the model's robustness and superior predictive capability in complex and dynamic urban traffic environments.

Source: IET Intelligent Transport Systems

DOI: 10.1049/itr2.70047

Authors: Zihao Wei, Ke Zhang, Shen Li, Meng Li

Institution: Tsinghua University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,790