| Journal of Information and Communications Technology:
Algorithms, Systems and Applications
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 (Arun 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]
Foundational AI / General models are
shown in Table 1 whereas Table 2 shows Traffic-specific models / Applications. 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
Model / Method
Application
Key Contributions & Limitations
2003
Seasonal ARIMA
Traffic
forecasting
Easy-to-understand statistical method; poor
with nonlinear patterns.
[15]
2006
VAR
Multivariate
forecasting
Limited for large, nonlinear traffic systems;
captured time-series dependencies.
[16]
2014
Spectral GCN
Graph learning
Introduced graph convolutions, later adapted
to spatiotemporal traffic.
[17]
2014
Random Forest +
Conformal
Prediction
Regression
forecasting
Added uncertainty estimation; not optimized
for dynamic traffic data.
[18]
2015
Seasonal ARIMA
(limited data)
Short-term
traffic prediction
Improved ARIMA, but could not capture
spatiotemporal dynamics.
[19]
2015
SVR + Adaptive
GA
Tourist flow
prediction
ML showed efficacy; lacked deep
spatiotemporal modeling.
[20]
2020
Deep Learning
(IoV)
Traffic flow (IoV)
DL on IoT-based traffic; challenges in real-
time deployment.
[21]
2020
Graph Attention
Temporal
ConvNet
Traffic speed
Early GNN for traffic; combined graph and
temporal convolution.
[22]
2020
ML approaches
Traffic prediction
(ITS)
Highlighted importance of deep learning for
ITS.
[23]
2020
ML models
ITS
Basic ML for traffic; lacked spatiotemporal
depth.
[24]
2020
Online Learning
Congestion
prediction
Adaptive models less accurate than DL.
[25]
2021
Improved GRU
Short-term
prediction
Enhanced GRU; lacked graph structure.
[26]
2021
Attention-LSTM +
GA
License plate
data
High accuracy; computationally heavy.
[27]
2021
LSTM Encoder–
Decoder
Long-term traffic
Strong temporal modeling; no explicit spatial
learning.
[28]
2021
Dynamic GCN
Point cloud
mining
Extended GCN to dynamic graphs; relevant
for spatial learning but not traffic-specific.
[29]
2022
LSTM-GRU
Hybrid
Urban speed
prediction
Integrated heterogeneous sources; lacked
graph structure.
[30]
2022
GCN-GAN
Traffic flow
prediction
Computationally intensive; GCN+GAN
Hybrid.
[31]
Year
Model / Method
Application
Key Contributions & Limitations
2022
Survey of DL in
Traffic
Review
Spatiotemporal hybrid taxonomy for
traffic.
[32]
2022
ML algorithms
review
ITS
Focused on real-time traffic management.
[33]
2023
Dynamic GCN
(chemical
reactor)
Point cloud
Method applicable to traffic; geometric
feature learning.
[34]
2025
CNN-GRU-LSTM
hybrid
Traffic flow
Combines spatial and temporal models;
trend toward complex approaches.
[35]
2025
STPFormer
Traffic dynamics
Integrates temporal encoding, spatial
sequence learning, graph matching, and
attention; strong generalization.
[36]
2025
SFADNet
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. Keyword co-occurrence
network visualization is shown in Fig. 1.
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.
Fig. 2: Keyword co-occurrence overlay visualization.
3.3 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.
4. 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. 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. Analysis of CNN, LSTM/GRU, and GCN-
GRU is shown in Table 3.
Fig. 3: Keyword density visualization. *(Node Size: Frequency of Keywords | Edge Thickness: Co-occurrence of
Strength | Color Gradient (Blue →Yellow): Year (2021→ 2023).
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.)
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
5. 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.
6. 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.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The datasets generated and/or analyzed during the current study that support the findings are available from
the corresponding author upon reasonable request.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
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