动态时空记忆增强网络用于交通预测
Huibing Zhang1, Qianxin Xie1, Zhaoyu Shou2
1Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
我们开发了一个动态时空记忆增强网络 (DSTMAN),用于更智能的流量预测. 该模型通过捕捉复杂的空间和时间交通模式,显著提高了准确性.
科学领域:
- 人工智能的人工智能
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 智慧城市的发展在很大程度上依赖于准确的交通流量预测.
- 现有的模型与复杂的时空动态,层次的时间特征和空间异质性作斗争.
- 有效的交通管理需要先进的模型来捕捉这些细微差别.
研究的目的:
- 引入一个新的模型,DSTMAN,用于增强流量预测.
- 解决捕捉动态时空背景和空间异质性的局限性.
- 提高交通流量预测的准确性和效率.
主要方法:
- 开发了三种时空嵌入,以捕捉动态背景和编码时间/空间特征.
- 集成嵌入到多尺度块中,用于分层的时空依赖提取.
- 引入了用于自适应邻近图的元记忆节点银行和学习空间异质性的二次记忆机制.
主要成果:
- 在公共数据集 (METR-LA,PEMS-BAY) 上,DSTMAN的表现优于MTGNN,DCRNN和AGCRN等基准模型.
- 实现了显著的平均绝对误差 (MAE) 减少:4%与MTGNN相比,6.9%与DCRNN相比,以及5.8%与METR-LA上的AGCRN相比.
- 在管理时空相关性和空间异质性方面表现出卓越的表现.
结论:
- 对于复杂的流量预测任务,DSTMAN提供了强大的解决方案.
- 该模型的新架构有效地捕捉了复杂的时空关系.
- DSTMAN代表了智能交通系统和智慧城市倡议的重大进步.
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