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.

Graphical Abstract

Novelty Statement

This systematic review article analyzes existing studies on GCN-GRU-based traffic flow prediction and compares them with CNN and LSTM/GRU models.