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城市交通流量预测的面向空间时间异质性的图形卷积网络
Xuan Li1, Muyang He1, Dong Qin2
1School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种新的以空间时间异质为导向的图形卷积网络 (SHGCN),用于准确的城市交通预测. 通过整合空气质量数据,该模型大大提高了交通流量预测的准确性.
科学领域:
- 城市车辆特设网络 (VANET)
- 交通预测和预测
- 图形卷积网络 (GCN)
背景情况:
- 城市车辆特设网络 (VANET) 利用跨领域数据进行增强的交通预测.
- 数据的空间和时间异质性使得标准化和预测模型的构建变得复杂.
- 动态外部因素对交通模式预测产生累积影响.
研究的目的:
- 提出面向空间时间异质性的图形卷积网络 (SHGCN),以应对城市交通预测的挑战.
- 利用空间多样性和空气质量等外部因素来改善交通预测.
- 通过混合GCN-GRU模型研究交叉相关性特征.
主要方法:
- 开发了SHGCN来分析交通流相关的空间异质性.
- 综合空气质量数据作为街道交通预测的外部因素.
- 采用混合图形卷积网络 (GCN) 和门式循环单元 (GRU) 模型来捕捉交叉相关性特征.
主要成果:
- 与基线模型相比,SHGCN模型表现出显著的改进,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 降低了2. 91%至41. 26%.
- 废弃研究证实,结合空气质量因素可以提高交通预测的性能.
- 该模型有效地捕捉了空气污染物,交通动态和道路网络拓之间的复杂关系.
结论:
- 拟议的SHGCN有效地处理城市交通数据的时空异质性.
- 整合空气质量数据可以提高交通预测模型的准确性和稳定性.
- SHGCN方法提供了一种有效的方法来理解城市交通系统中的复杂关系.
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