Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 1235-1247· 0 citations
TL;DR
An intelligent framework is developed as a hybrid one, where four complementary learners are used to operate in parallel: a spatio-temporal graph neural network (ST-GNN) to represent dependencies between travel zones, a Transformer to represent long-horizon temporal patterns, an LSTM to represent sequential mobility dynamics, and an XGBoost to represent structured demographic and cost characteristics.
Abstract
Almost all operations of a smart transportation or tourism platform are based on reliable travel demand forecasts, including fleet allocation and dynamic pricing. The information that is now at hand to do the task, trip records, accommodation logs, demographic surveys, are large and heterogeneous, a combination of spatial structure and long-range temporal dependence with sequential behavior and tabular properties that cannot be handled by any single model family. In this paper, an intelligent framework is developed as a hybrid one, where four complementary learners are used to operate in parallel: a spatio-temporal graph neural network (ST-GNN) to represent dependencies between travel zones, a Transformer to represent long-horizon temporal patterns, an LSTM to represent sequential mobility dynamics, and an XGBoost to represent structured demographic and cost characteristics. A stacked meta-learner amalgamates the four branch predictions, and a Q-learning agent transforms predictions into adaptive transport and accommodation suggestions with a reward based on satisfaction-minus-cost. The fused model, tested on the New York City TLC taxi-trip corpus with demographic information, achieves lower RMSE than either ARIMA (0.214) or the best single learner (0.169) and higher R² (0.91), and the policy recommendation policy converges in about 300 episodes. In addition to accuracy, the framework provides a formal fusion architecture, a full algorithmic specification, and a statistical validation protocol, and provides a scalable and interpretable platform to intelligent mobility analytics.
Precise short-term travel demand forecasting and personalized trip advice are two of the most challenging problems in intelligent transportation systems in part due to the fact that traveler behavior is influenced concurrently by spatial configuration, time dynamics, socio-demographic environment, and cost sensitivity. The majority of the current models cover these dimensions separately. The current paper suggests an end-to-end, monolithic framework that pairs the learning of multimodal representations with a spatio-temporal graph neural network (ST-GNN), a Transformer-based forecasting module, a causal inference layer, and a Q-learning recommender. Heterogeneous travel history, such as demographics, trip purpose, mode of transport, cost of accommodation, and temporal attitudes are co-embedded and propagated through graph convolutions and multi-head self-attention to generate multi-horizon destination-level demand prediction. An actionable do-calculus layer measures the impact of traveler characteristics on decisions and the policy implications guide a reinforcement learning agent that modulates transport and accommodation suggestions to traveler groups. The ST-GNN + Transformer forecaster achieves a 25% lower RMSE than an ARIMA baseline on a publicly available dataset of approximately 8,700 traveler records and, in comparison to the LSTM and DCRNN alternatives, converges within just 300 training episodes. The framework provides a replicable base to adaptable, understandable model of travel behavior in smart-city and tourism analytics contexts.
Santosh Kumar Sharma, Satish Chander, Piyush Gupta· International journal of com...· 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 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
Railway transportation plays a critical role in supporting sustainable mobility in Indonesia, yet significant fluctuations in passenger demand often lead to congestion and operational challenges. This study presents a systematic evaluation of decomposition-based forecasting frameworks for railway passenger demand prediction by integrating Seasonal-Trend Decomposition using Loess, EMD applied to residual components, and Fuzzy C-Means clustering. Using the Argo Muria train service as a case study, multiple deep learning models, including LSTM, GRU, RNN, CNN, and BiLSTM, are trained on decomposed components, and their forecasts are combined linearly. Model performance is evaluated using a rolling-origin strategy across multiple stations. At the primary destination station, Semarang-Gambir, the best configuration achieves an MAE of 19.88, RMSE of 26.79, sMAPE of 8.97, and R2 of 0.84. Consistent results across stations demonstrate the framework's robustness and generalization capability.
Iqbal Kharisudin, Merlinda Lavenia· Operations Research and Deci...· 0 citations
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.
X. N. Xie, W. L. Zhang· Advanced Electromagnetics· 0 citations
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