发表机构
Hellenic Mediterranean University; National Technical University of Athens（希腊地中海大学; 雅典国立技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对智能边检系统队列管理问题，提出多模态AI框架，结合LSTM、MPC等技术，经评估可降低预测误差与等待时间、提升吞吐量。
AI 中文摘要
本研究提出了一种高效的边检管理流程。与主要基于静态数据运行的现有队列管理系统相比，该方案考虑了动态交通状况，即使在不确定情况下也能实现最优性能。为此，我们提出了一种适配边检系统需求的多模态人工智能（AI）框架，可实现实时队列预测、管理及资源优化。该新颖方案整合了异构数据源，并通过统一表示呈现这些数据，采用长短期记忆（LSTM）网络进行队列预测。此外，它利用模型预测控制（MPC）和调度优化来推导可执行的控制策略，这些策略可呈现给边检人员。本研究使用模拟现实交通的合成数据进行评估，结果显示，与ARIMA方法和基于规则的方法相比，所提方法将队列预测误差降低了高达35%，平均等待时间缩短了30%，平均吞吐量提高了近20%。上述结果表明，将AI架构与优化技术相结合用于主动自适应边检交通管理是有效且高效的。
英文摘要
In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are based on mostly static data, the proposed work takes into account dynamic traffic conditions, thus enabling optimal performance, even in cases of uncertainty. To this end, we are proposing a multi-modal Artificial Intelligence (AI) framework, tailored to th needs of border control systems, which enables real-time queue prediction, management, and resource optimization. The novel proposed approach integrates heterogeneous data sources and presents them through a unified representation by employing Long Short-Term Memory (LSTM) networks for queue forecasting. Furthermore, it leverages Model Predictive Control (MPC) and scheduling optimization to derive actionable control policies, which in turn can be presented to border control officers. The proposed work has been evaluated using synthetic data simulating realistic traffic. The evaluation results demonstrate that the proposed method reduces queue prediction error by up to 35% and average waiting time by 30%. Accordingly, the average throughput increases by nearly 20%, compared to ARIMA and rule-based methods. The abovementioned results show the effectiveness and efficiency of combining AI architectures with optimization techniques for proactive and adaptive border traffic management.
Comments6 pages, 2 figures, conference
