Received: 28 June 2025; Revised: 08 September 2025; Accepted: 12 September 2025; Published Online: 15 September 2025.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2025, 1(2), 25309 | Volume 1 Issue 2 (September 2025) | DOI: https://doi.org/10.64189/ict.25309
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
GCN-GRU for Junction-Level Traffic Flow Prediction: A
Systematic Review and Comparative Synthesis with CNN
and LSTM/GRU
Anidhya Mandot,
1
Shilpa Kanthalia,
1
Arun Vaishnav
2,*
and Manuj Joshi
2
1
Faculty of Management Studies, JRN, Rajasthan Vidhyapeeth (Deemed to be University), Udaipur, Rajasthan, 313001, India
2
Faculty of Computing and Informatics, Sir Padampat Singhania University, Udaipur, Rajasthan, 313601, India
*Email: a.vaishnav2155@gmail.com (A. Vaishnav)
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.
Keywords: Traffic flow prediction; Graph Convolutional Networks; GRU; CNN; LSTM; Intelligent transportation systems.
1. Introduction
Urban traffic congestion is one of the most pressing challenges of the twenty-first century, impacting quality of life,
economic productivity, mobility, and environmental sustainability.
[1,2]
Accurate traffic flow forecasting has therefore
become central to Intelligent Transportation Systems (ITS), particularly at junctions where multiple flows converge
and diverge, creating bottlenecks and congestion.
[3,4]
Junction-level prediction is especially critical since these points
concentrate the complexity of road networks and often determine overall traffic dynamics.
[5]
Traditional models such
as regression-based methods, Kalman filters, and ARIMA struggled with the nonlinear and high-dimensional nature
of modern traffic data.
[6]
The learning method offered advances through CNNs for spatial feature extraction and RNN
variants (LSTM, GRU) for temporal dynamics.
[5,7]
However, key challenges remain: traffic networks are inherently
non-Euclidean, with road segments and intersections forming irregular graphs rather than grids; sensor data is often
sparse or missing; and models trained in one domain may not generalize well under shifting traffic patterns. These
limitations highlight the need for approaches that jointly capture temporal sequences and network topology while
remaining robust to incomplete and heterogeneous data. This gap is filled by the development of Graph Convolutional
Networks (GCNs),
[8]
which allow representation learning on graph-structured data directly. The resulting GCN-GRU
hybrid models can concurrently capture temporal dynamics of traffic flow and spatial dependencies across irregular
road networks when combined with recurrent architectures like GRU. GCN-GRU is positioned as one of the most
promising methods for junction-level traffic prediction by this synthesis. To map the research landscape, synthesize
recent advancements, and critically compare GCN-GRU approaches against CNN and LSTM/GRU models, the study
performs a Systematic Literature Review (SLR) in conjunction with bibliometric analysis. This review offers a
comprehensive view of the current state of research and potential future directions in junction-level traffic prediction
by combining quantitative bibliometric mapping with qualitative synthesis.
[9]
The development of traffic prediction methods demonstrates a distinct paradigm shift from statistical to deep learning.
For short-term prediction, classical models like ARIMA and Kalman filtering provided interpretable answers, but they
were unable to keep up with the growing complexity of real-world, multi-source traffic data. Deep learning models
have been adopted more quickly as a result of the growth of large-scale traffic sensing infrastructure, which includes
GPS, loop detectors, and IoT-enabled vehicle sensors. Although CNN-based models excel at capturing spatial
dependencies, they are unable to accurately depict irregular urban road networks due to their reliance on grid-structured
inputs. In contrast, LSTM and GRU models are very good at predicting temporal sequences and identifying long-term
dependencies in traffic flow, but they are unable to explicitly model the spatial relationships between intersections. By
directly learning spatial representations from graph-structured road networks, GCN-based techniques overcome this
gap. The GCN-GRU hybrid architecture is specifically designed for junction-level prediction tasks by combining gated
recurrent units for temporal sequence modeling with graph convolution for spatial correlation learning.
[10]
To illustrate this methodological evolution, the review summarizes the comparative strengths and weaknesses of CNN,
LSTM/GRU, and GCN-GRU models in traffic flow prediction. This tabular synthesis demonstrates how each model
family addresses certain challenges while leaving others unresolved, thereby highlighting the rationale for GCN-GRU
hybrid approaches.
Table 1: Foundational AI / General models.
Year
Authors
Model / Method
Application
Key Contributions & Limitations
1986
Rumelhart et al.
Backpropagation
Neural Network
General AI
foundation
Backpropagation was introduced, enabling deep model
training, but initially limited to time-series data.
[11]
1991
Elman
Simple Recurrent
Network (SRN)
Sequential
learning
Early RNN for sequences; struggled with long-term
dependencies.
[12]
1997
Hochreiter &
Schmidhuber
LSTM
Sequential
modeling
Solved vanishing gradient; laid groundwork for time-
series prediction, including traffic.
[13]
2016
Goodfellow et al.
Deep Learning (MIT
Press)
General
Full DL reference; introduced CNN/LSTM concepts
applied later to traffic modeling.
[14]
Table 2: Traffic-specific models / Applications.
Year
Authors
Application
Key Contributions & Limitations
2003
Williams & Hoel
Traffic forecasting
Easy-to-understand statistical method; poor
with nonlinear patterns.
[15]
2006
Zivot & Wang
Multivariate
forecasting
Limited for large, nonlinear traffic systems;
captured time-series dependencies.
[16]
2014
Bruna et al.
Graph learning
Introduced graph convolutions, later adapted to
spatiotemporal traffic.
[17]
2014
Johansson et al.
Regression
forecasting
Added uncertainty estimation; not optimized
for dynamic traffic data.
[18]
2015
Kumar & Vanajakshi
Short-term traffic
prediction
Improved ARIMA, but could not capture
spatiotemporal dynamics.
[19]
2015
Chen et al.
Tourist flow
prediction
ML showed efficacy; lacked deep
spatiotemporal modeling.
[20]
2020
Chen et al.
Traffic flow (IoV)
DL on IoT-based traffic; challenges in real-time
deployment.
[21]
2020
Guo & Yuan
Traffic speed
Early GNN for traffic; combined graph and
temporal convolution.
[22]
2020
Li & Xu
Traffic prediction
(ITS)
Highlighted importance of deep learning for
ITS.
[23]
2020
Meena et al.
ITS
Basic ML for traffic; lacked spatiotemporal
depth.
[24]
2020
Manibardo et al.
Congestion
prediction
Adaptive models less accurate than DL.
[25]
2021
Shu et al.
Short-term
prediction
Enhanced GRU; lacked graph structure.
[26]
2021
Tang et al.
License plate data
High accuracy; computationally heavy.
[27]
2021
Wang et al.
Long-term traffic
Strong temporal modeling; no explicit spatial
learning.
[28]
2021
Xing et al.
Point cloud
mining
Extended GCN to dynamic graphs; relevant for
spatial learning but not traffic-specific.
[29]
2022
Zafar et al.
Urban speed
prediction
Integrated heterogeneous sources; lacked graph
structure.
[30]
2022
Zheng et al.
Traffic flow
prediction
Computationally intensive; GCN+GAN
Hybrid.
[31]
2022
Yin et al.
Review
Spatiotemporal hybrid taxonomy for traffic.
[32]
2022
Modi et al.
ITS
Focused on real-time traffic management.
[33]
2023
Xing et al.
Point cloud
Method applicable to traffic; geometric feature
learning.
[34]
2025
Singh et al.
Traffic flow
Combines spatial and temporal models; trend
toward complex approaches.
[35]
2025
Fang et al.
Traffic dynamics
Integrates temporal encoding, spatial sequence
learning, graph matching, and attention; strong
generalization.
[36]
2025
Wu et al.
Traffic flow
Fused graph with cross-attention; outperforms
state-of-the-art on large datasets.
[37]
2. Methodology
This systematic review article that analyzes existing studies on GCN-GRU-based traffic flow prediction and compares
them with CNN and LSTM/GRU models. To ensure methodological transparency and reproducibility, this review
adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Using
keyword combinations like "traffic flow prediction," "junction-level traffic," "GCN," "GRU," "CNN," and "LSTM,"
extensive searches were carried out in Web of Science, Scopus, and IEEE Xplore. A total of 432 studies published
between 2017 and 2025 were found in the first search. 87 papers were kept for the final synthesis after duplicates were
eliminated and relevance was checked using keywords, abstracts, and titles.
[38]
In parallel, bibliometric mapping was carried out to examine citation clusters, co-authorship patterns, and keyword co-
occurrence using VOSviewer. A thorough grasp of the research landscape is ensured by this dual approach, which
combines quantitative breadth and qualitative depth through systematic literature synthesis backed by bibliometric
analysis.
Additionally, Lens.org's dataset-level statistics, which included 3,846 scholarly works with over 97,000 citations, 315
works cited by patents, and 579 citing patents, validated the scope and significance of this field of study. This highlights
the field's industrial and applied significance in addition to its academic maturity.
[39]
2.1 Systematic review approach
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework is followed in this
review, guaranteeing methodological rigor, reproducibility, and transparency. Web of Science, Scopus, and IEEE
Xplore were the databases that were searched.
Keywords like "traffic flow prediction," "junction-level traffic," "GCN," "GRU," "CNN," and "LSTM" were combined
in the search strategy.
• The screening procedure 432 studies (2017–2024) were initially retrieved.
• 87 papers were kept for synthesis after duplicates were eliminated and titles, abstracts, and keywords were
screened.
[40]
2.2 Bibliometric mapping and data insights
Bibliometric analysis tool: VOSviewer.
Dimensions analyzed:
• Co-authorship patterns.
• Keyword co-occurrence.
• Citation clusters.
Dataset-level statistics (Lens.org):
• 3,846 scholarly works retrieved.
• 97,000+ total citations.
• 315 works cited by patents.
• 579 citing patents, highlighting industrial adoption.
Significance: Confirms both academic maturity and practical relevance of junction-level traffic prediction research.
[41]
2.3 Threats to validity
This study may be affected by selection bias, as the datasets used for evaluation may not fully represent all traffic
conditions or geographic regions. Dataset coverage is another potential limitation, since the chosen datasets may not
include variations across different times, seasons, or unusual traffic events. These factors could limit the
generalizability of the proposed model to other traffic environments. Future work should include more diverse datasets
to address these threats and strengthen the validity of the findings.
[42]
2.4 Research questions
Recent advances in deep learning have significantly transformed junction-level traffic flow prediction, evolving from
traditional time-series models to sophisticated spatiotemporal architectures. In this context, GCN-GRU hybrid models
have emerged as a powerful alternative to standalone CNN or LSTM/GRU approaches by jointly capturing spatial
dependencies and temporal dynamics. Bibliometric analysis reveals evolving trends in authorship, prevalent keywords,
and citation patterns, reflecting the growing interest in enhanced and attention-based models. Despite these advances,
research gaps remain in model generalization, explainability, and real-time deployment, pointing to promising future
directions for further exploration.
[43,44]
3. Results of bibliometric analysis
3.1 Co-authorship network
The co-authorship analysis shows that Chinese and U.S. institutions have made the most contributions to the field of
GCN-based traffic prediction. There are many strong national research clusters, but there aren't many international
collaborations. This means that there needs to be more cross-continental engagement and data sharing. This kind of
collaboration would make it easier to use models in different cities.
Fig. 1: Keyword co-occurrence network visualization.
While the co-occurrence network highlights thematic concentrations and methodological trends, the underlying co-
authorship patterns reveal relatively weak international linkages. This limited cross-regional collaboration may restrict
access to diverse traffic datasets, which are essential for validating models across different urban contexts. Without
stronger dataset sharing and global cooperation, the transferability of traffic prediction models across cities remains
constrained, potentially reducing their effectiveness in heterogeneous real-world environments.
3.2 Keyword co-occurrence map
The keyword co-occurrence mapping shows three main research groups:
Cluster 1: GCN, graph neural networks, and modeling of space and time.
Group 2: LSTM, GRU, and predicting time series.
Cluster 3: CNN, modeling based on images, and a grid-based representation.
This shows that there has been a big change from CNN and RNN models to graph-based architectures. The increasing
popularity of GCN-GRU keywords is a clear sign that this model is becoming the best way to predict junction levels.
Fig. 2 shows the keyword co-occurrence overlay visualization, highlighting how research emphasis has shifted from
broader themes such as “deep learning,” “application,” and “survey” toward more recent and specialized topics like
“graph convolutional network (GCN),” “dependency,” and “spatial correlation” between 2021 and 2023. This
progression reflects the field’s movement from general methodological foundations to advanced modeling of non-
Euclidean traffic structures. However, the fragmented distribution of keywords across clusters also indicates limited
cross-regional integration, reinforcing the finding from co-authorship analysis that weak international collaboration
may hinder dataset sharing and reduce the transferability of models across diverse urban contexts.
4.3 Citation clusters
Citation analysis identifies seminal GCN-based models like DCRNN and ST-GCN as central nodes, which are heavily
cited in later research. Recent studies that combine GCN with GRU show an increase in citation momentum, which
shows that more people are recognizing their predictive power. In contrast, models that only use CNN and LSTM show
fewer citations, which means they are becoming less important in cutting-edge traffic prediction research.
Fig. 3 presents the keyword density visualization, where brighter areas highlight the most frequently cited and co-
occurring terms such as “graph convolutional network (GCN),” “graph,” “deep learning,” and “application.” These
dense regions indicate the dominant methodological focus of recent research, with strong emphasis on graph-based
modeling and spatiotemporal dependencies. However, the density remains uneven across clusters, reflecting the
influence of a limited number of research hubs. Combined with the weak international linkages observed in co-
authorship networks, this suggests barriers to dataset sharing and knowledge transfer across regions, potentially
constraining the generalizability of models to cities with different traffic infrastructures and mobility patterns.
Collaboration patterns in traffic prediction research, such as combining graph-based spatial learning with recurrent
temporal models (e.g., CNN-GRU-LSTM hybrids or GCN-based approaches), enhance model adaptability to diverse
datasets. Models trained using multi-source data or collaborative frameworks generally exhibit better transferability
across different traffic environments. This suggests that integrating diverse collaboration patterns improves robustness
when applying models to unseen traffic scenarios.
Fig. 2: Keyword co-occurrence overlay visualization.
Fig. 3: Keyword density visualization. *(Node Size: Frequency of Keywords | Edge Thickness: Co-occurrence of Strength
| Color Gradient (Blue →Yellow): Year (2021→ 2023).
5. Comparative analysis of CNN, LSTM/GRU, and GCN-GRU
When it comes to junction-level traffic prediction tasks, the comparative evidence clearly favours GCN-GRU models.
Although CNNs are good at modeling spatial features, their ability to do so is constrained by the presumption of
Euclidean grid structures. Road network topology is not explicitly taken into account by LSTM and GRU, despite their
superiority at capturing sequential dependencies.
Table 3: Analysis of CNN, LSTM/GRU, and GCN-GRU.
Model
Strengths
Limitations
Suitability for junction-
level prediction
CNN
Effective with grid-based traffic
data, it captures spatial features.
Limited for dynamic
junctions and weak at
temporal dependencies
Moderate
LSTM/GRU
Strong sequence modeling and
exceptional long-term temporal
dependency
Not good at finding spatial
correlations; uses a lot of
computing power
Moderate
GCN-GRU
Combines temporal (sequence)
and spatial (graph) learning; it is
scalable to intricate road networks.
Needs big labeled datasets;
hard to understand
High
(*Detailed benchmark results are shown in Table 4; values may vary due to differences in datasets, traffic conditions, and
evaluation protocols. Researcher-Generated Analysis of CNN, LSTM/GRU, and GCN-GRU Models.)
By combining the advantages of both paradigms, GCN-GRU hybrids achieve improved accuracy and reduced RMSE
on benchmark datasets like METR-LA and PEMS-BAY. Importantly, because graph-based representations enable
information to spread among connected nodes, GCN-GRU exhibits resilience against missing data and sensor failure.
GCN-GRU is a strong option for scalable, practical ITS deployment because of these benefits.
Table 4: Benchmarking of traffic prediction models.
Model / Method
RMSE Range
MAE Range
Caveats
Seasonal ARIMA
15–25
10–18
Limited to linear patterns; poor for nonlinear dynamics
LSTM
10–18
7–12
Strong temporal modeling, lacks explicit spatial awareness
GCN-based models
9–16
6–11
Strong spatial modeling, needs robust graphs
CNN-GRU-LSTM hybrid
7–13
5–9
Strong spatiotemporal modeling, computationally heavier
STPFormer
6–12
4–8
Excellent generalization, complex architecture
SFADNet
5–11
4–7
Robust across datasets, requires cross-attention fusion
*Researcher-Generated Comparative Performance of Traffic Flow Prediction Models
6. Discussion and research gaps
The review indicates out a number of important research gaps. First, even though GCN-GRU models perform better
than their predecessors, model interpretability is still a major problem. The majority of GCN-GRU architectures
operate as opaque black boxes, despite the fact that transportation authorities need clear and understandable models to
inform operational choices. Second, there are still problems with computational scalability because accurate yet
lightweight models are needed for real-time deployment in big urban networks. Third, there are questions regarding
generalizability to developing nations with distinct traffic dynamics due to the dependence on datasets from a small
number of regions (most notably China and the U.S.).
The integration of multi-modal data sources represents another gap. Current models frequently only use data on traffic
flow or speed, but weather, ride-sharing, and event data could all be added to improve junction-level prediction. The
synergy of GCN-GRU with edge computing or federated learning, which are essential for real-time ITS applications
under data privacy constraints, has only been briefly examined in a few studies.
Explainability techniques such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-
agnostic Explanations) can identify the relative importance of spatiotemporal features in traffic prediction. Their
deployment enhances model transparency, improves trust among end-users, and assists in diagnosing model failures.
In real-world traffic systems, such explainability methods support informed decision-making, regulatory compliance,
and system debugging.
7. Conclusion and future scope
GCN-GRU hybrids have emerged as the cutting-edge paradigm for junction-level traffic flow prediction, as evidenced
by this comprehensive literature review and bibliometric mapping. Although it sacrifices interpretability and
computational efficiency, GCN-GRU is more adaptive to irregular road networks and multi-junction interactions than
CNN and LSTM/GRU. Expanding cross-regional studies, facilitating scalable real-time deployment, and improving
model interpretability should be the main goals of future research. A promising area is the combination of GCN-GRU
with edge computing frameworks, reinforcement learning, and attention mechanisms. Incorporating these models into
smart city infrastructures can also lead to better urban mobility, less environmental impact, and proactive congestion
management. In conclusion, the evidence clearly indicates that GCN-GRU represents the next step forward for
intelligent transportation systems, providing a route to more precise, reliable, and scalable solutions for future cities,
even though CNN and LSTM/GRU established the groundwork for deep learning in traffic prediction.
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing
or editing of the manuscript and no images were manipulated using AI.
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