English(EN) A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems

人工智能框架通过实时队列预测增强边境管控

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新颖的多模态人工智能框架，旨在通过实时队列预测和管理来增强边境控制系统。该框架整合了多样化的数据源，利用长短期记忆（LSTM）网络进行准确的队列预测。它进一步采用模型预测控制（MPC）和优化技术，为边境管制官员生成可操作的策略。使用模拟数据进行的评估表明，与传统的ARIMA和基于规则的方法相比，预测误差降低了35%，平均等待时间减少了30%，平均吞吐量增加了近20%。 AI

影响该框架可以通过利用人工智能进行预测性管理，显著提高边境口岸的效率并缩短等待时间。

排序理由这是一篇详细介绍新颖人工智能框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Varvara Mama, Eleni Veroni, Nikolaos Kapsalis, Christos D. Nikolopoulos, Anargyros T. Baklezos · 2026-08-28 04:00

面向智能边境控制系统中实时队列预测、管理和优化的多模态人工智能框架

arXiv:2608.27010v1 Announce Type: new Abstract: 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 traff…

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面向智能边境控制系统中实时队列预测、管理和优化的多模态人工智能框架

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