用AI实时预测和优化边境排队，提升通关效率。
A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems
- 融合多源数据与LSTM模型，实现动态排队预测。
- 预测误差降低35%，平均等待时间减少30%。
- 适合智能边检系统、交通调度研究者参考。
本文提出一种面向智能边检系统的多模态人工智能框架，用于实现实时排队预测、管理与资源优化。相较于依赖静态数据的传统系统，该框架引入动态交通条件，通过长短期记忆网络（LSTM）整合异构数据并生成统一表征，结合模型预测控制（MPC）与调度优化，生成可操作的调控策略，供边检人员使用。基于模拟真实交通的合成数据评估显示，该方法将排队预测误差降低最多35%，平均等待时间减少30%，相比ARIMA与规则基方法，平均吞吐率提升近20%。结果表明，AI架构与优化技术的融合能有效实现主动、自适应的边检流量管理。
原文摘要 · Abstract (English)
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.
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