What type of study is this?

This is a Systematic Review study (also classified as: Bibliometric Analysis).

October 15, 2025

GCN-GRU for Junction-Level Traffic Flow Prediction: A Systematic Review and Comparative Synthesis with CNN and LSTM/GRU

Key Points

GCN-GRU shows notable improvements in junction-level traffic flow prediction compared to traditional models.
The comparative synthesis found GCN-GRU offers significant advantages for processing complex urban traffic data.
A systematic literature review utilized bibliometric mapping to analyze keyword co-occurrence and citation impacts.
Current challenges for GCN-GRU include issues of scalability, interpretability, and real-world deployment in smart cities.

Abstract

For contemporary intelligent transportation systems, precise junction-level traffic flow prediction is crucial. Models like Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU) have been thoroughly researched since deep learning gained popularity. The ability to capture both spatial and temporal dependencies in traffic data has recently been demonstrated by combining Graph Convolutional Networks (GCN) with GRU. This study combines comparative synthesis of CNN, LSTM/GRU, and GCN-GRU approaches with bibliometric mapping to present a systematic literature review (SLR) of recent works on traffic flow prediction. Three viewpoints—keyword co-occurrence, co-authorship networks, and citation impact clusters—were mapped using VOS viewer bibliometric analysis. In comparison to conventional CNN and LSTM/GRU, our synthesis shows that GCN-GRU offers notable gains in processing complex urban traffic junction data. Open issues like scalability, interpretability, and deployment in actual smart city platforms are also noted in the review.

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