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: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
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 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.
By Jianing Long, Xiaobin Li, Wuming Lei, Weiguang Wang
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.
By Faraz Shamim (KIST Medical College and Teaching Hospital, Nepal), Faris Shamim (OTH Regensburg)
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.
By Dan Zhu, Chi Sin Ng, Litian Xie, Yang Liu
arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
By Ting Gao, Elvin Isufi, Winnie Daamen, Erik-Sander Smits, Serge Hoogendoorn
The paper introduces WZPlanner, a new dataset and model for safe autonomous driving in work zones. The dataset, WorkZonePlan, contains over 149,000 synthetic and 5,000 real-world samples with 3D annotations for lane and work zone boundaries, plus 228 evaluation routes in CARLA. The proposed BoundaryFormer (BF) and its enhanced BF++ variants jointly predict lane/work‑zone boundaries and driving trajectories, achieving higher Driving Scores while being significantly smaller than competing models.
By Nishad Sahu (Raj), Changzhong Qian (Raj), Guangzhou Cai (Raj), Shounak Sural (Raj), Ragunathan (Raj), Rajkumar
arXiv:2607. 09090v1 Announce Type: new Abstract: In large-scale ride-hailing, hold control is a critical mechanism for improving passenger-driver experience.
By Xu Liu, Kai Wan, Zihao Lu
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: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
RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.
By Guangyu Wang, Zhidan Liu