Vehicle exhaust emissions at signalized intersections are the essential source of traffic-related pollution to pedestrians. Therefore, it is critical to predicting traffic emissions, especially the hazardous CO gas, with practical and accurate methods. However, the CO emission and concentration at crosswalks can be influenced by the complex traffic conditions in a complicated way, making the prediction of CO concentration a challenging task for traditional statistical models. To this end, a hybrid machine learning framework is proposed in this study to investigate the concentration of CO emissions at pedestrian crosswalks. The proposed method firstly ranks key influencing factors with a random forest approach. Then a prediction model with Multi-Variate Long Short-Term Memory (LSTM) neural networks based on the selected factors is developed. Data is collected at the field intersection for model training and validation. The autoregressive integrated moving average (ARIMA), support vector machines (SVM), radial basis functions network (RBFN), nonlinear vector autoregressive (VAR) and gated recurrent unit (GRU) neural network are selected as the benchmark models to verify the performance of the proposed model. The Root Mean Square Errors (RMSE), Mean Absolute Error (MAE) and R square are calculated to evaluate the performance of models comprehensively. The results indicated that the proposed model overwhelms the benchmark models in terms of prediction accuracy.

信号交叉口的汽车尾气排放是与行人相关的交通污染的重要来源。因此，使用实用且准确的方法来预测交通排放，尤其是危险的CO气体至关重要。但是，人行横道的二氧化碳排放量和浓度会受到复杂交通状况以复杂方式的影响，因此对于传统统计模型而言，二氧化碳浓度的预测是一项艰巨的任务。为此，在本研究中提出了一种混合机器学习框架，以研究人行横道上的CO排放浓度。该方法首先采用随机森林方法对关键影响因素进行排序。然后，基于所选因素，建立了具有多变量长期短期记忆（LSTM）神经网络的预测模型。在野外交叉点收集数据以进行模型训练和验证。自回归综合移动平均值（ARIMA），支持向量机（SVM），径向基函数网络（RBFN），非线性向量自回归（VAR）和门控递归单元（选择GRU神经网络作为基准模型，以验证所提出模型的性能。计算均方根误差（RMSE），均方根绝对误差（MAE）和R方，以全面评估模型的性能。结果表明，所提出的模型在预测准确性方面压倒了基准模型。