使用深度学习架构准确的V2X流量预测.
Ali R Abdellah1, Ahmed Abdelmoaty1, Abdelhamied A Ateya2,3
1Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena, Egypt.
Frontiers in artificial intelligence
|April 3, 2025
概括
这项研究引入了一种新的深度学习 (DL) 方法,使用双向长期短期记忆 (BiLSTM) 网络来预测车辆到一切 (V2X) 通信系统中的流量,显著提高了准确性.
科学领域:
- 智能运输系统 智能运输系统
- 深度学习用于交通预测.
- V2X 通信网络 通信网络
背景情况:
- 车辆到一切 (V2X) 通信为提高道路安全和效率提供了潜力.
- 目前的V2X系统在数据共享和网络可靠性方面面临挑战,这阻碍了广泛采用.
- 准确的流量预测对于优化V2X功能至关重要.
研究的目的:
- 提出和评估一种新的深度学习 (DL) 方法,用于V2X环境中的流量预测.
- 在V2X通信中解决数据共享和网络可靠性的挑战.
- 通过改进交通预测,提高运输系统的效率和安全性.
主要方法:
- 实现双向长短期内存 (BiLSTM) 网络用于交通模式预测.
- 对BiLSTM与其他深度学习架构 (如单向LSTM和Gated Recurrent Unit (GRU)) 的比较分析.
- 在模拟的V2X场景中评估预测准确度和性能指标.
主要成果:
- 与其他DL架构相比,双向长期短期内存 (BiLSTM) 模型在流量模式预测方面表现出卓越的准确性.
- 提出的DL方法有效地解决了V2X中的数据共享和网络可靠性问题.
- 增强的流量预测导致更高效的资源配置和提高网络性能.
结论:
- 深度学习,特别是BiLSTM网络,为V2X通信中准确的流量预测提供了强大的解决方案.
- 改进的交通预测能力有助于提高道路安全,减少燃料消耗和减少排放.
- 这些发现支持开发更可持续,更有效的智能交通系统.
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