Chen Hui, Huang Jian, Lu Yong, Huang Jijie
School of Computer and Artificial Intelligence, Foshan University, Foshan, 528225, China.
School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
Sci Rep. 2025 Jul 23;15(1):26732. doi: 10.1038/s41598-025-11072-0.
The Urban traffic flow is affected by both internal supply and demand changes and external random disturbances, and during its continuous spatiotemporal propagation, these factors overlap with each other, presenting a highly non-linear and complex spatiotemporal pattern, which poses a huge challenge to traffic flow prediction. In response to the above challenges, this paper proposes a novel Spatio-Temporal Graph neural network with Multi-timeScale (abbreviated as STGMS). In STGMS, a multi-timescale feature decomposition strategy was designed to decompose the traffic flow into signals at multiple timescales and residuals. A unified spatio-temporal feature encoding module was designed to integrate the spatiotemporal features of traffic flow and the interaction features of multi-timescale traffic flows. Finally, the mapping from the multi-timescale spatiotemporal feature encoding to the future traffic flow was learned. We conducted numerous experiments on four real-world datasets and compared them with eleven baseline models from the past three years. The results show that the performance of our model outperforms the current state-of-the-art baseline models. On the four datasets, the average improvement rates of the three prediction accuracy metrics, namely the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), reach 17.69%, 15.65%, and 10.30% respectively.
城市交通流受到内部供需变化和外部随机干扰的双重影响，并且在其持续的时空传播过程中，这些因素相互重叠，呈现出高度非线性和复杂的时空模式，这给交通流预测带来了巨大挑战。针对上述挑战，本文提出了一种新颖的多时间尺度时空图神经网络（简称为STGMS）。在STGMS中，设计了一种多时间尺度特征分解策略，将交通流分解为多个时间尺度的信号和残差。设计了一个统一的时空特征编码模块，以整合交通流的时空特征和多时间尺度交通流的交互特征。最后，学习了从多时间尺度时空特征编码到未来交通流的映射。我们在四个真实世界数据集上进行了大量实验，并将其与过去三年的十一个基线模型进行了比较。结果表明，我们模型的性能优于当前最先进的基线模型。在这四个数据集上，三个预测精度指标，即平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）的平均提升率分别达到17.69%、15.65%和10.30%。
