arXiv Machine Learning August 5, 2026 By Yinxiao Zhang, Sen Wang, Yi Gao

Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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.

By Juhwan Lee, Seokbin Yoon, Keumjin Lee, Hojong Baik, Seyeon Jung

llms efficiency

arXiv Machine Learning

Aug 4

DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments

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 Machine Learning

Jun 8

Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions

arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.

By Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft, Filipe Rodrigues

arXiv AI

Jun 24

EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

arXiv:2512. 15067v4 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning.

By Zijiang Yan, Yixiang Huang, Jianhua Pei, Hina Tabassum, Luca Chiaraviglio

diffusion

arXiv Machine Learning

1d ago

From Recoverability to Functional Use: Auditing Temporal Reports in Time-Series Forecasting

arXiv:2608. 10433v4 Announce Type: replace Abstract: Time-series forecasters increasingly accompany numerical predictions with explicit temporal reports, such as delays or selected history, but a correct report need not describe the information actually used by the forecast.

By Qipeng Qian, Yuntao Qian

safety

arXiv AI

Jul 7

KAN vs LSTM Performance in Time Series Forecasting

arXiv:2511. 18613v2 Announce Type: replace-cross Abstract: This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (LSTM) networks for the forecasting of stochastic, non-stationary financial time series.

By Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova, Federico Frascoli

benchmarks safety

Related stories

Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints

DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments

Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions

EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

From Recoverability to Functional Use: Auditing Temporal Reports in Time-Series Forecasting

KAN vs LSTM Performance in Time Series Forecasting