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可持续城市流动的深度强化学习:一个图书法和经验审查
Sharique Jamal1, Farheen Siddiqui1, M Afshar Alam1
1Department of Computer Science, School of Engineering Science and Technology, Jamia Hamdard, New Delhi 110062, India.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了智能城市中人工智能 (AI) 的计算整合框架 (CIF). 它验证了城市移动的AI,显示了显著的交通效率增长和减少排放.
科学领域:
- 计算整合框架 (CIF) 是一个计算整合框架.
- 人工智能 (AI) 是一种人工智能.
- 智慧城市应用 智慧城市应用
- 城市流动性 城市流动性
背景情况:
- 图书统计分析确定城市流动性是人工智能实施的成熟领域.
- 现有的人工智能框架缺乏用于智能城市应用程序的系统集成.
研究的目的:
- 为计算集成框架 (CIF) 提供经验基础.
- 为了证明AI的可行性,特别是深度强化学习 (DRL),优化城市流动.
- 将人工智能技术与城市应用领域联系起来.
主要方法:
- 为域名选择进行大规模的文献识别分析.
- 计算集成框架 (CIF) 的开发和验证.
- 实施深度强化学习 (DRL) 驱动的交通信号控制系统.
主要成果:
- CIF验证了可持续城市流动的DRL,平均等待时间减少了约48%,交通效率提高了30%以上.
- 联邦DRL保持96%的中央性能,同时保持数据隐私.
- 观察到燃料消耗和二氧化碳排放的显著减少.
结论:
- 基于证据的域名选择通过文献计量是有效的.
- CIF可以作为一个可行的AI决策支持桥梁,用于城市应用.
- 对于可持续的城市流动性而言,DLR是计算上可行的,与全球可持续发展目标保持一致.
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