arXiv:2608. 02950v1 Announce Type: new Abstract: Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger arrival times at security checkpoints.
By Yinxiao Zhang, Sen Wang, Yi Gao
The paper proposes a multi‑modal AI framework for real‑time queue prediction, management, and resource optimisation in border control systems. It integrates heterogeneous data sources using LSTM networks for forecasting and applies Model Predictive Control and scheduling optimisation to generate actionable policies for officers. Evaluation on synthetic traffic data shows up to 35% reduction in prediction error, 30% lower average waiting time, and nearly 20% higher throughput compared to ARIMA and rule‑based methods.
By Varvara Mama, Eleni Veroni, Nikolaos Kapsalis, Christos D. Nikolopoulos, Anargyros T. Baklezos
arXiv:2608. 00402v1 Announce Type: new Abstract: Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems.
By Yanming Lyu, Yue Cheng, Lingkun Li, Ruipeng Gao, Xinyue Liu, Hui Gao, Qiang Ni
arXiv:2512.08281v2 Announce Type: replace-cross
Abstract: Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the...
By Kyungmin Kim, Seokbin Yoon, Keumjin Lee
The study explores machine learning and deep learning techniques to predict travel time for transportation and logistics within supply chain systems. By leveraging extensive historical data, it aims to build an accurate model that estimates travel times for inventory movement. The research emphasizes the importance of precise travel time predictions for improving logistics consistency, performance, and planning across the supply chain.
By Balaji Venkateswaran
arXiv:2606. 11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs.
By Alex Porcayo, Yutian Pang, Maria Thomas, John-Paul Clarke