智能交通系统的车辆网络中的多目标联合学习交通预测
Arulmurgan Aalavanthar1, Famila S2, Shanmugam Sundaramurthy1
1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, India.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一种新的多目标联合学习方法,用于准确预测货运运输的速度. 这种方法通过允许在没有中央服务器的情况下进行协作培训来增强流量管理策略,提高预测准确性和资源利用率.
科学领域:
- 运输科学 运输科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的时空预测未来的货运运输速度对于有效的都市交通管理至关重要.
- 目前的方法通常依赖于集中式数据处理,这对隐私和效率构成挑战.
研究的目的:
- 开发一种使用多目标联合学习进行货运运输速度预测的新方法.
- 通过改进的时空预测来加强交通管理策略.
主要方法:
- 实施了参与者之间的协作培训方法,避免了集中式云服务器.
- 利用强化学习来实现动态决策和使用图形模型来识别交通强度.
- 集成的联合学习与多目标优化,用于准确的交通模式预测.
主要成果:
- 在交通速度方面实现了97.45%的预测准确度.
- 显示了23.4%的通信延迟和92.45%的数据包交付比率 (PDR).
- 通过可视化展示了大都市区和邻近城市之间的互联连接的有效捕捉.
结论:
- 拟议的多目标联合学习方法显著优于现有的货运运输速度估计方法.
- 该方法提供了更好的通信效率,数据包交付和资源利用.
- 这种方法为理解和管理复杂的都市交通动态提供了一个强大的框架.
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