Accurate traffic Flow Prediction can assist in traffic management, route planning, and congestion mitigation, which holds significant importance in enhancing the efficiency and reliability of intelligent transportation systems (ITS). However, existing traffic flow prediction models suffer from limitations in capturing the complex spatial-temporal dependencies within traffic networks. In order to address this issue, this study proposes a multi-segment fusion tensor graph convolutional network (MS-FTGCN) for traffic flow prediction with the following three-fold ideas: a) building a unified spatial-temporal graph convolutional framework based on Tensor M-product, which capture the spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and weekly components to model multi temporal properties of traffic flows, respectively; c) fusing the outputs of the three components by attention mechanism to obtain the final traffic flow prediction results. The results of experiments conducted on two traffic flow datasets demonstrate that the proposed MS-FTGCN outperforms the state-of-the-art models.

翻译：准确的交通流预测有助于交通管理、路径规划和拥堵缓解，对于提升智能交通系统的效率与可靠性具有重要意义。然而，现有交通流预测模型在捕捉交通网络内复杂的时空依赖性方面存在局限。为解决这一问题，本研究提出一种用于交通流预测的多段融合张量图卷积网络，其核心思想包含三个方面：a) 基于张量M积构建统一的时空图卷积框架，以同步捕捉时空模式；b) 分别引入小时、日、周分量以建模交通流的多时段特性；c) 通过注意力机制融合三个分量的输出，从而获得最终的交通流预测结果。在两个交通流数据集上进行的实验结果表明，所提出的MS-FTGCN模型性能优于现有最先进模型。