College of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin, China.
PLoS One. 2024 Jun 12;19(6):e0305424. doi: 10.1371/journal.pone.0305424. eCollection 2024.
Urban traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), which can enhance traffic efficiency and ensure public safety. However, predicting urban traffic flow faces numerous challenges, such as intricate temporal dependencies, spatial correlations, and the influence of external factors. Existing research methods cannot fully capture the complex spatio-temporal dependence of traffic flow. Inspired by video analysis in computer vision, we represent traffic flow as traffic frames and propose an end-to-end urban traffic flow prediction model named Spatio-temporal Decoupled 3D DenseNet with Attention ResNet (ST-D3DDARN). Specifically, this model extracts multi-source traffic flow features through closeness, period, trend, and external factor branches. Subsequently, it dynamically establishes global spatio-temporal correlations by integrating spatial self-attention and coordinate attention in a residual network, accurately predicting the inflow and outflow of traffic throughout the city. In order to evaluate the effectiveness of the ST-D3DDARN model, experiments are carried out on two publicly available real-world datasets. The results indicate that ST-D3DDARN outperforms existing models in terms of single-step prediction, multi-step prediction, and efficiency.
城市交通流预测在智能交通系统（ITS）中起着至关重要的作用，它可以提高交通效率，确保公共安全。然而，预测城市交通流面临着许多挑战，例如复杂的时间依赖性、空间相关性和外部因素的影响。现有的研究方法无法充分捕捉交通流的复杂时空依赖性。受计算机视觉中视频分析的启发，我们将交通流表示为交通帧，并提出了一种名为时空解耦 3D 密集网络与注意力残差网络（ST-D3DDARN）的端到端城市交通流预测模型。具体来说，该模型通过接近度、周期、趋势和外部因素分支提取多源交通流特征。然后，它通过在残差网络中集成空间自注意力和坐标注意力来动态建立全局时空相关性，准确预测整个城市的交通流量的流入和流出。为了评估 ST-D3DDARN 模型的有效性，我们在两个公开可用的真实数据集上进行了实验。结果表明，ST-D3DDARN 在单步预测、多步预测和效率方面均优于现有模型。
