智能停车场使用数字双胞胎的多目标优化框架,以应对智能城市的MDP和PSO
Dinesh Sahu1, Priyanshu Sinha2, Shiv Prakash3
1SCSET, Bennett University, Plot Nos 8, 11, TechZone 2, Greater Noida, Uttar Pradesh, 201310, India.
Scientific reports
|March 5, 2025
概括
本研究介绍了使用数字双胞胎,帕雷托前线,MDP和PSO的智能停车框架,以优化城市移动性. 这种新的方法显著减少了智能城市的搜索时间,能源消耗和交通拥堵.
科学领域:
- 城市规划和智能城市技术
- 优化算法和人工智能的人工智能
- 交通管理系统 交通管理系统
背景情况:
- 现有的智能停车系统在资源管理,可扩展性和实时适应方面扎.
- 停车管理效率低下会导致交通拥堵和城市地区能源消耗增加.
研究的目的:
- 提出一个智能停车场多目标优化框架 (MOFPSP),集成数字双胞胎,帕雷托前端优化,马尔科夫决策过程 (MDP) 和粒子群优化 (PSO).
- 通过尽量减少搜索时间,能源消耗和交通干扰,同时最大限度地提高停车位的可用性,提高智能停车系统的效率.
主要方法:
- 数字双胞胎技术:为实时系统估计创建虚拟模型.
- 帕雷托前方优化:多目标优化以平衡竞争目标 (例如,最小化搜索时间,最大化可用性).
- 马尔科夫决策过程 (MDP) 和粒子群集优化 (PSO):用于实时决策和完善全球分发的解决方案.
主要成果:
- 拟议的框架显示了与现有算法相比的显著改进.
- 搜索时间减少了25%,能源使用提高了18%,交通拥堵减少了30%.
- 通过包括搜索时间,能源消耗,拥堵水平,可扩展性和利用率在内的关键指标进行评估.
结论:
- 混合优化和实时决策框架为先进的智能停车管理提供了一个有前途的解决方案.
- 该研究强调了集成先进计算技术的潜力,以改善智慧城市的城市流动性和资源效率.
关键词:
数字双胞胎技术的数字双胞胎技术节能停车解决方案 节能停车解决方案马尔科夫决策过程 (MDP)多目标优化多目标优化巴雷托前方优化粒子集群优化 (PSO) 是一个资源分配 资源分配安全的安全的安全的安全的安全.智慧城市 智慧城市智能停车系统 智能停车系统交通拥堵管理 交通拥堵管理更多相关视频
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