This study presents a deep learning–enabled framework for real-time dynamic route optimization in logistics systems, addressing fundamental limitations of traditional static routing and heuristic-based decision approaches.
Abstract
As transportation networks grow increasingly complex and data-rich, the need for intelligent, adaptive routing mechanisms has become essential for efficient and resilient mobility operations. This study presents a deep learning–enabled framework for real-time dynamic route optimization in logistics systems, addressing fundamental limitations of traditional static routing and heuristic-based decision approaches. The proposed architecture integrates long short-term memory (LSTM) networks with spatio- temporal graph convolutional networks (ST-GCN) to model nonlinear temporal evolution and spatial dependencies in traffic flows, GPS trajectories, meteorological conditions, and road network structures. By capturing these complex patterns, the predictive module generates highly accurate short-term forecasts of congestion levels and delivery delays, which are subsequently incorporated into an adaptive routing engine that continuously updates vehicle paths in response to evolving network conditions. Comprehensive preprocessing of multimodal traffic and environmental datasets, advanced feature engineering, and supervised training of the LSTM and ST-GCN models are employed. Model performance is assessed via mean absolute error (MAE), root mean square error (RMSE), and ROC–AUC. Experimental results show substantial gains over baseline predictors and conventional routing: a 45.6% reduction in MAE, a 39.5% reduction in RMSE, and an ROC–AUC of 0.91 for delay prediction, while enabling an estimated 12.3% reduction in carbon emissions. These improvements translate into measurable reductions in travel time and fuel consumption, underscoring the system’s potential to enhance operational resilience, environmental sustainability, and decision efficiency.
This paper proposes a real-time dynamic vehicle path optimization framework for urban logistics, driven by advanced deep reinforcement learning techniques. The study describes the urban vehicle routing problem as a Markov decision process, integrating fleet operations, dynamic traffic conditions, and constantly arriving customer orders from heterogeneous realtime data streams. A graph-based neural network architecture for capturing complex spatio-temporal dependencies. This enables the system to learn and adapt to rapidly changing urban routing strategies. Both synthetic and real-world datasets are extensively tested. The proposed methods significantly reduce the operational cost and delivery latency. These methods are very effective compared to adaptive heuristic algorithms and traditional machine learning baselines. Maintaining robust performance under high traffic fluctuation and demand uncertainty is crucial for spatio-temporal feature extraction and network architecture optimization. The study demonstrates the feasibility and effectiveness of deep reinforcement learning technology in large-scale real-time logistics optimization in cities, and provides an important reference for the application of intelligent data-driven scheduling and path planning systems in complex urban networks.
Jinyan Wang, Hong-Juan Cong· International Conference on...· 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
Results confirm that explicitly coupling traffic prediction with online trajectory replanning enables more adaptive and efficient navigation under time-varying traffic conditions.
Hassan Haghighi, Maamar El Amine Hamri, D. Asadi et al.· Data Science for Transportat...· 1 citation
A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.
Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al.· International journal of res...· 0 citations
Urban traffic congestion poses a growing challenge to efficient mobility, and existing forecasting and routing methods often struggle to support timely decisions in dynamic road networks. To address this issue, this study proposes a hybrid framework that combines a spatiotemporal graph attention network with the Quantum Approximate Optimization Algorithm (QAOA) for joint congestion prediction and path optimization. In the prediction stage, a multi-scale spatiotemporal encoder is developed to capture short-term, intra-day, and multi-day traffic variation patterns, while an adaptive graph learning mechanism is introduced to model hidden spatial correlations among road segments. In the optimization stage, the routing problem is formulated as a QUBO model and solved by an improved deep QAOA with hierarchical parameter sharing, which helps stabilize training and improve solution quality. The predicted congestion probabilities are further incorporated into the routing objective, enabling the optimization module to avoid highly congested areas. Experiments on the PeMS-BAY and METR-LA datasets show that the proposed method reduces the RMSE of 15-min traffic prediction by 5.4% compared with the strongest baseline. For 50-node routing tasks, it achieves an optimality ratio of 0.952, outperforming standard QAOA. In congestion-aware routing, the average travel time is further reduced by 13.7%. These results indicate that the proposed framework is effective for integrated traffic prediction and dynamic path planning.
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
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