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基于机器学习的物联网系统,用于智能城市的安全交通管理和事故检测
Saravana Balaji Balasubramanian1, Prasanalakshmi Balaji2, Asmaa Munshi3
1Department of Information Technology, Lebanese French University, Erbil, Iraq.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种自适应交通管理 (ATM) 系统,配有事故警报音响系统 (AALS) 和安全数据传输 (SEE-TREND),以减少智能城市的拥堵和事故. 该ATM系统的性能明显优于传统方法,改善了交通流量和安全性.
科学领域:
- 智能运输系统 智能运输系统
- 智慧城市技术 智慧城市技术
- 机器学习应用 机器学习应用
背景情况:
- 智慧城市的快速城市化和车辆数量的增加导致严重的交通拥堵,污染和事故.
- 现有的交通管理系统很难有效地处理动态的交通条件,并确保数据安全.
- 每年,道路交通事故造成大量死亡和长期损伤.
研究的目的:
- 开发和评估与事故警报声系统 (AALS) 集成的自适应交通管理 (ATM) 系统,并确保数据传输安全.
- 为了减轻交通拥堵,提高道路安全,并确保智能城市环境中的安全数据传输.
- 通过智能解决方案提高城市交通系统的整体效率和体验.
主要方法:
- 实施基于物联网的交通管理系统,利用自动驾驶汽车和智能设备中的传感器.
- 开发一种自适应交通管理 (ATM) 模型,根据实时流量和预测的流动量动态调整交通信号时间.
- 集成安全的与交通相关事件早期检测 (SEE-TREND) 协议,用于安全的数据传输和事故检测.
主要成果:
- 与传统方法相比,拟议的ATM系统在减少旅行时间和缓解交通拥堵方面取得了显著的改进.
- 该系统通过不断修改信号时间以实现无过渡,有效地管理流量.
- 集成的AALS和SEE-TREND组件增强了事故检测,并确保了安全的数据传输,为更安全的旅行体验做出了贡献.
结论:
- 适应式交通管理 (ATM) 系统与AALS和SEE-TREND相结合,为智慧城市交通规划提供了卓越的解决方案.
- 这种创新方法显著减少了交通拥堵,减少了车辆等待时间,并降低了事故发生率.
- 该系统增强了整体城市旅行体验,为更高效,更安全的智能城市移动铺平了道路.
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