D-MGDCN-CLSTM:一个基于多图格式门式卷积和卷积长短内存的流量预测模型.
Linliang Zhang1,2, Shuyun Xu3, Shuo Li4
1Shanxi Intelligent Transportation Laboratory Co., Ltd., Taiyuan 030036, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究介绍了D-MGDCN-CLSTM,这是一种新的交通预测模型,可以有效处理缺失的数据,并捕捉短期和长期的交通模式. 该模型显著提高了预测准确度,超过了现有方法.
科学领域:
- 智能运输系统 智能运输系统
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的交通预测对于城市规划和拥堵管理至关重要.
- 部分数据丢失和流量数据的双重性 (短期波动和长期趋势) 对现有模型构成重大挑战.
研究的目的:
- 开发一个强大的交通预测模型,能够解决数据丢失和捕获双重时间特征.
- 为了提高实时交通预测的准确性和可靠性.
主要方法:
- 一个新型的模型,D-MGDCN-CLSTM,集成多图门扩展卷积 (MGDCN) 和卷积长短期记忆 (ConvLSTM).
- 使用离散波形变形 (DWT) 进行多尺度分解,以分离短期和长期的交通模式.
- 采用DTWN算法来归纳丢失的流量数据.
主要成果:
- 与PeMSD7 ((M) 和PeMSD7 ((L) 数据集上的其他10个算法相比,D-MGDCN-CLSTM模型表现出优异的性能.
- 在平均绝对误差 (MAE),根平均平方误差 (RMSE) 和精度 (ACC) 中实现了平均改进.
- 废弃性研究和参数分析证实了拟议的分解方法的有效性.
结论:
- D-MGDCN-CLSTM模型有效地处理缺失的数据,并在交通预测中捕获双重时间特征.
- 拟议的方法在实时交通预测准确性和可靠性方面取得了重大进展.
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