arXiv：cs.AI· Varvara Mama, Fani D. Christidi, Nikolaos C. Kapsalis, Anargyros T. Baklezos, Christos D. Nikolopoulos·· 16 小时前
基于 LightGBM 的多时间跨度可解释排队预测与资源感知边境管控决策支持
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM
AI 导读
一项研究基于 LightGBM 为边境口岸构建多时间跨度预测与决策支持架构，针对 15、30、60、120 分钟分别训练回归器，联合预测等待时间与排队长度。模型使用时间滞后的交通、服务、密度与排队特征，并通过分位数 LightGBM 给出 95% 预测区间。运营层纳入车道兼容性、警员可用性、旅客类别与预注册信息，输出机器可读建议、告警与聚合 KPI，结果形成可复现的合成数据基线。
正文
Abstract:Border crossing points are stochastic service systems in which traffic demand, traveler categories, registration status, inspection requirements, staffing, and lane availability interact on multiple time scales. This paper extends an earlier multimodal queue-management framework by introducing an implementation-oriented, multi-horizon forecasting and decision-support architecture based on Light Gradient Boosting Machine (LightGBM) models. In contrast to a single forecasting block, the proposed approach constructs horizon-specific regressors for 15, 30, 60, and 120 min forecasts and jointly estimates future waiting time and queue lengths. The models operate on temporally lagged traffic, service, density, and queue features, while an operational layer incorporates lane compatibility, officer availability, possibly degraded resources, traveler category, preregistration information, and human-approval constraints. Quantile LightGBM models provide 95\% prediction intervals for waiting-time forecasts. Beyond prediction, the framework produces machine-readable recommendations, alerts, aggregate key performance indicators, confidence information, and integration outputs suitable for border resource-management and decision-support systems. The results establish a reproducible synthetic-data baseline and a practical interface for subsequent validation with operational border-crossing data.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15930 [cs.AI] |
| (or arXiv:2610.15930v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15930 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Christos Nikolopoulos Nikolopoulos [view email]
[v1]
Sun, 11 Oct 2026 21:27:20 UTC (1,364 KB)
来源：arXiv：cs.AI · arxiv.org