Tourist flow modeling and prediction in scenic areas is highly complex, exhibiting significant spatiotemporal dependence and being influenced by various external factors. Traditional models struggle to simultaneously characterize complex spatiotemporal relationships and integrate diverse external information, posing challenges for intelligent resource management and dynamic information scheduling in large-scale networked systems. To address this, this paper proposes a closed-loop integration framework that combines a particle swarm optimization (PSO)-based Temporal Fusion Transformer-Graph Attention Network (TFT-GAT) prediction model with Deep Double-Q Network (D3QN) scheduling optimization. The framework integrates heterogeneous data such as historical traffic, weather, and social media, achieving adaptive time-varying spatial embedding through a graph attention network (GAT) while capturing long- and short-term dependencies using a Temporal Fusion Transformer (TFT) for both point and quantile prediction. The PSO algorithm performs global optimization of the TFT-GAT hyperparameters, and the resulting prediction outputs together with uncertainty estimates are incorporated into the D3QN to realize closed-loop online capacity allocation based on reinforcement learning. Such a data-driven spatiotemporal modeling strategy also provides methodological insights for dynamic information fusion and adaptive resource scheduling in intelligent electromagnetic sensing and communication environments. Experiments conducted at the Forbidden City in Beijing demonstrate high prediction accuracy with a mean absolute error of 1.5–2.6 people/hour, an average quantile coverage exceeding 82%, and a response time of 140.6 ms. The incorporation of exogenous factors, particularly holidays, improves prediction performance by 18.4%, validating the proposed framework’s robustness in spatiotemporal coupling, uncertainty representation, and real-time scheduling.
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.
Ahmed Abdel-Wahab Rakha, Mohammed S. A. Elsersy· Informatica· 0 citations
Decentralised and adaptive operation of urban drainage networks (UDNs) is increasingly required to contain pluvial
flooding and pumping-energy costs under intensifying and uncertain rainfall. Existing UDN studies have largely
addressed layout design or post-event leakage diagnosis in isolation, while the closed loop that links short-horizon
hydraulic forecasting to energy-aware pump/gate control under uncertainty remains under-explored. This paper
proposes an integrated framework that couples a Graph-Augmented Spatio-Temporal Forecaster (GASTF) with a
novel Hybrid Coati–Osprey Synergistic Optimization Algorithm (COSOA) for the real-time control (RTC) of
UDNs. The UDN is encoded as a weighted graph; the GASTF predicts node water levels from lagged levels, graphaggregated neighbour states and rainfall, and supplies these forecasts to a robust multi-objective RTC formulation
that jointly minimises flood volume and pumping energy while maximising hydraulic reliability across a rainfall
ensemble. COSOA fuses the structured group-hunting exploration of the Coati Optimization Algorithm with the
plunge-and-carry exploitation of the Osprey Optimization Algorithm, governed by an adaptive synergy factor and a
memetic elite-refinement operator. On eight 30-dimensional benchmark functions COSOA attains the best mean
rank (2.12) among nine optimizers. On a SWMM-calibrated RTC problem, COSOA reduces storm flood volume by
94.9% (from 33,163 m³ under passive operation to 1,702 m³) and raises hydraulic reliability from 47.7% to 97.8%,
achieving the best mean robust objective (J = 0.0046) and the lowest single-run objective among all baselines, with
statistically significant gains over the Osprey, genetic and search-and-rescue optimizers (Wilcoxon p < 0.01). Graph
augmentation lowers forecasting RMSE by 5.1% over a non-graph neural baseline (R² = 0.961). The results indicate
that the COSOA-driven framework is an effective and robust tool for energy-aware, flood-resilient UDN operation
J. Suganthi, I. Jayasimman· International Journal of Dru...· 0 citations
This work studies multi-horizon taxi demand prediction as the task of learning a mapping from past observations to multiple future demand values under temporal dependence and uncertainty, and suggests that successful multi-horizon forecasting requires global temporal interaction and explicit quantification of uncertainty.
Magesh Rajakumar, C. Markarian, S. Atalla· Proceedings of the 15th Inte...· 0 citations
Reliable traffic flow forecasting is a core component of intelligent transportation systems; however, many current approaches are still unable to simultaneously model spatial interdependencies and long-term temporal correlations, particularly in cross-sea corridors that exhibit directional heterogeneity and pronounced temporal variability. This study aims to develop an accurate and stable traffic flow prediction framework for cross-sea corridors. To achieve this, an HGTransformer model was proposed that constructed a hypergraph from traffic nodes based on spatial proximity and correlated flow variations, and used hypergraph convolution to extract spatial node representations. These representations were then fed into a Transformer equipped with multi-head self-attention and positional encoding, enabling the model to capture global temporal dependencies in the evolution of traffic flow. Using hourly traffic flow data from the Huangmaohai cross-sea corridor, the model was tested on 1 to 4 h forecasting horizons and compared with long short-term memory (LSTM), multi-layer perceptron (MLP), random forest (RF), support vector regression (SVR), and Bayesian regression (BR) models. The proposed model achieved the best overall performance, with average mean absolute percentage error (MAPE), mean absolute error (MAE), weighted mean absolute percentage error (WMAPE), and root mean square error (RMSE) of 0.178, 13.375, 0.140, and 20.538, respectively. At the 1 h horizon, these values further decreased to 0.172, 12.678, 0.132, and 19.401, while preserving peak–valley structures more accurately under both short- and longer-horizon forecasting. The main contribution of this study lies in the systematic application and validation of the Huangmaohai Corridor dataset, including a reproducible hypergraph construction strategy tailored specifically for this particular infrastructure.
Fan Jiang, Zhiyong Ma, Pumulo Mukozomba et al.· Applied Sciences· 0 citations
An integrated prediction-and-visualisation pipeline that transforms complex data distributions into actionable visual analytics, such as interpretable station-to-station demand heatmaps via interactive GIS Folium layers is implemented, providing an operationally robust framework to support smart-city transportation management and build more sustainable urban transit systems.
Berna Çalışkan· Journal of Data Analytics an...· 0 citations
This study proposes a structurally simple, low-node-density disjunctive graph model, then performs feature extraction using Graph Neural Networks (GNNs), and finally optimize convergence using the Migrating Birds Optimization—an intelligent optimization algorithm proven effective for scheduling decision problems.
Ze Zhao, Mingyan Jiang, Feng Wang· International Conference on...· 0 citations