arXiv AI
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM
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arXiv AI
Aug 28
A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems
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.
arXiv Machine Learning
Jun 9
Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints
arXiv:2606. 07622v1 Announce Type: new Abstract: Accurate passenger queue forecasting in airport terminals is essential for efficient departure operations, as it enables proactive congestion management.
arXiv Machine Learning
Aug 5
Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput
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.
arXiv AI
Oct 7
When better traffic forecasts fail to improve signal control: a layered diagnostic study of forecast-to-decision value
The study examines why improved traffic forecasts do not always lead to better signal‑control outcomes. Using 29 days of reconstructed traffic data from Xuancheng, China, the authors evaluate various forecasting methods—including point forecasts, conformal intervals, and dependence‑aware scenarios—within a closed‑loop control framework. While entry‑level and movement‑level forecasts reduce mean absolute error by about 4%, the resulting signal‑control decisions sometimes worsen queue vehicle‑seconds and spillback exposure, highlighting gaps between forecast accuracy and operational benefit.
arXiv Machine Learning
Oct 7
Machine Learning for German Redispatch Forecasting under Data Delays and Temporal Distribution Shift
The study evaluates probabilistic machine‑learning models for forecasting German grid redispatch volumes under data‑delay constraints. Using 48,242 records from 2021‑2024, boosted‑tree LightGBM with rolling calibration achieved the best performance (nWIS 0.7767), outperforming autoregressive and seasonal baselines. Neural models with zero‑censored outputs performed similarly but revealed undercoverage during high‑volume events.
arXiv Machine Learning
Aug 4
Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods
arXiv:2608. 01180v1 Announce Type: new Abstract: Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity.