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Open Access

ARTICLE

Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System

Pangwei Wang1,*, Jie Wang1, Zipeng Wang1, Hangrui Dong2, Li Wang1
1 Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing, China
2 Beijing Connected and Autonomous Vehicles Technology Co., Ltd., Beijing, China
* Corresponding Author: Pangwei Wang. Email: email
(This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.080815

Received 15 February 2026; Accepted 27 April 2026; Published online 18 May 2026

Abstract

Traffic holographic perception refers to the real-time, high-fidelity, and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors, including cameras, radars, and connected vehicle data. The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System (ITS). However, sensors are vulnerable to environmental interference, which can result in data loss at specific points or along arterial highways for certain periods, potentially undermining system safety and decision-making reliability. To address these challenges, a deep learning method based on Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) is proposed, leveraging Artificial Intelligence (AI) and intelligent connected technologies for real-time acquisition of multi-sensor perception data. A feature-level fusion integrates multi-source perception data. GCN captures spatial dependencies from the road network topology, while GRU extracts temporal features from time series, enabling accurate imputation of missing traffic data. The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area. Results show that the accuracy of long-term traffic state completion reaches 89.36%, and the Root Mean Square Error (RMSE) is reduced by 17.2% compared to the Long Short-Term Memory (LSTM) baseline. This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS.

Keywords

Intelligent transportation; information security; traffic information completion; traffic holographic perception; AI-driven; edge computing; graph convolutional neural network

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