2026· IEEE Open Journal of Intelligent Transportation Systems· Vol 7, pp. 1712-1728· 0 citations· 47 references
TL;DR
A novel approach that leverages aggregated cellular network activity as a large-scale, infrastructure-independent data source for predicting travel time and speed and proposes the Integrated Mobility Model, a multimodal fusion architecture that combines cellular network activity with Bluetooth sensor data to further enhance prediction accuracy.
Abstract
Accurate city-wide traffic prediction is essential for intelligent mobility management in modern urban environments. This work introduces a novel approach that leverages aggregated cellular network activity as a large-scale, infrastructure-independent data source for predicting travel time and speed. We first develop the Network Insight Model, which demonstrates that cellular activity patterns alone can effectively capture city-wide traffic dynamics. We then propose the Integrated Mobility Model, a multimodal fusion architecture that combines cellular network activity with Bluetooth sensor data to further enhance prediction accuracy. Both models incorporate attention mechanisms, enabling interpretable insights into which intersections and road segments most strongly influence traffic conditions. Using real-world datasets from York Region, Canada, we evaluate our models against standard regression baselines and state-of-the-art deep learning approaches. Across multiple metrics, including MAE, RMSE, R2, MAPE, and the Travel Time Index (TTI), our models consistently achieve superior predictive performance while providing meaningful, attention-based explanations of traffic patterns.
Urban traffic congestion remains a critical challenge for smart city development, particularly in the context of real-time monitoring and prediction for intelligent transportation systems. To address this issue, this study proposes a Digital Twin-based urban traffic prediction framework using a lightweight Diffusion Convolutional Recurrent Neural Network (DCRNN-Lite). The proposed model integrates spatial dependencies among road segments through diffusion convolution and temporal traffic dynamics through recurrent modeling, enabling effective spatiotemporal traffic forecasting with reduced computational complexity. Experiments were conducted on the real-world METR-LA dataset, consisting of traffic speed data from 207 sensors deployed across the Los Angeles highway network. The experimental results demonstrate that DCRNN-Lite achieves stable prediction performance, as reflected by low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), consistent convergence behavior, and strong correlation between predicted and actual traffic speeds at both node and city-wide levels. Despite its simplified architecture, the model effectively captures local traffic variations and global mobility trends, making it suitable for real-time deployment. The findings indicate that the proposed approach provides a favorable balance between accuracy and efficiency, highlighting its potential as a core component for digital twin-enabled smart cities and metaverse-based urban traffic management systems.
H. Awad· International Journal Resear...· 0 citations
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
Chu-xia Chen· Proceedings of the 3rd Inter...· 0 citations
A multi-source machine learning framework for segment-direction-level prediction in the Denizli city center showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value.
Muhammed Enes Karaoğlan, Yetis Sazi Murat· Sustainability· 0 citations
TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
The results show the effectiveness of spatial graph learning, temporal convolution and adaptive attention in forecasting traffic speeds across the benchmark urban transportation datasets and provide a promising way to apply the proposed method in real scenarios.
T. Venkata, S. Vivek, Satheesh Kumar Sapabathy et al.· International Journal for Gl...· 0 citations
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