Abstract
Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diverse impacts of the various traffic observations (e.g. vehicle speed and road occupancy) on the traffic flow prediction, and different traffic observations can be considered as different channels of input features. We argue that the analysis in multiple-channel traffic observations might help to better address this problem. In this paper, we study the novel problem of multi-channel traffic flow prediction, and propose a deep \underline{M}ulti-\underline{V}iew \underline{C}hannel-wise \underline{S}patio-\underline{T}emporal \underline{Net}work (MVC-STNet) model to effectively address it. Specifically, we first construct the localized and globalized spatial graph where the multi-view fusion module is used to effectively extract the local and global spatial dependencies. Then LSTM is used to learn the temporal correlations. To effectively model the different impacts of various traffic observations on traffic flow prediction, a channel-wise graph convolutional network is also designed. Extensive experiments are conducted over the PEMS04 and PEMS08 datasets. The results demonstrate that the proposed MVC-STNet outperforms state-of-the-art methods by a large margin.
Abstract (translated)
准确预测交通流量对许多实际应用（包括公共安全和智能交通系统）至关重要。这个问题包括人和城市交通数据的动态运动模式以及复杂的空间-时间相关性。同时，大多数现有模型忽略了各种交通观察（如车辆速度和道路占用率）对交通流量预测的影响，而不同的交通观察可以被视为不同的输入特征。我们认为，在多通道交通观察分析中可能有助于更好地解决这个问题。在本文中，我们研究了多通道交通流量预测的新问题，并提出了一个深度 Multi-View Multi-Channel Temporal Network (MMC-STNet) 模型来有效地解决它。具体来说，我们首先构建了局部和全局空间图，其中多视图融合模块用于有效地提取局部和全局空间依赖关系。然后使用 LSTM 学习时间关联。为了有效地建模各种交通观察对交通流量预测的不同影响，还设计了一个通道级别的图卷积网络。在 PEMS04 和 PEMS08 数据集上进行了大量实验。结果表明，与最先进的 methods相比，所提出的 MMC-STNet 具有很大的优势。
URL
https://arxiv.org/abs/2404.15034
