Spatiotemporal PM2.5 forecasting technology plays an important role in urban traffic environment management and planning. In order to establish a satisfactory high-precision PM2.5 prediction model, a new multidata-driven spatiotemporal PM2.5 forecasting model is proposed in this paper. The overall modelling framework consists of three main parts. In part I, the graph convolutional network uses an adjacency matrix to effectively aggregate spatiotemporal pollutant data from different nodes and extract the most valuable feature information for target point modeling from the original data. In part II, the extracted feature information is used as the input of the gated recursive unit and the long short-term memory network to construct the prediction model. In part III, the Q-learning algorithm builds the best ensemble PM2.5 forecasting model by analyzing the processing ability and analysis ability of different predictors. Based on the analysis of multiple cases, the following conclusions can be drawn: (1) Graphic convolutional networks can effectively analyze the spatiotemporal correlation of PM2.5 data and achieve better performance than traditional convolutional neural networks. (2) Q-learning can adaptively optimize the ensemble weight coefficient and achieve better results than the traditional optimization algorithm. (3) The proposed GCN-LSTM-GRU-Q model can achieve better results than the 24 benchmark models.

PM2.5时空预测技术在城市交通环境管理和规划中发挥着重要作用。为了建立令人满意的高精度 PM2.5 预测模型，本文提出了一种新的多数据驱动的 PM2.5 时空预测模型。整个建模框架由三个主要部分组成。在第一部分中，图卷积网络使用邻接矩阵有效聚合来自不同节点的时空污染物数据，并从原始数据中提取最有价值的特征信息用于目标点建模。在第二部分中，将提取的特征信息作为门控递归单元和长短期记忆网络的输入，构建预测模型。在第三部分，Q-learning 算法构建了最好的集成 PM2。5 预测模型通过分析不同预测变量的处理能力和分析能力。基于对多个案例的分析，可以得出以下结论：（1）图形卷积网络可以有效分析PM2.5数据的时空相关性，取得比传统卷积神经网络更好的性能。(2) Q-learning可以自适应优化集成权重系数，取得比传统优化算法更好的效果。(3) 提出的 GCN-LSTM-GRU-Q 模型可以取得比 24 个基准模型更好的结果。5 数据并取得比传统卷积神经网络更好的性能。(2) Q-learning可以自适应优化集成权重系数，取得比传统优化算法更好的效果。(3) 提出的 GCN-LSTM-GRU-Q 模型可以取得比 24 个基准模型更好的结果。5 数据并取得比传统卷积神经网络更好的性能。(2) Q-learning可以自适应优化集成权重系数，取得比传统优化算法更好的效果。(3) 提出的 GCN-LSTM-GRU-Q 模型可以取得比 24 个基准模型更好的结果。