Aiming at the low accuracy of carbon emission prediction caused by the high volatility and nonlinearity of the carbon emission data series in transportation industry, a transportation carbon emission prediction model combining the secondary decomposition, dual attention mechanism, improved sparrow search algorithm (ISSA) and long short-term memory (LSTM) network is proposed. First, complete ensemble empirical mode decomposition with adaptive noise is introduced to decompose the transportation carbon emission data series into modal components with different frequencies, then sample entropy is used to quantify the complexity of each component, and secondary decomposition is performed on the component with the highest entropy value via variational mode decomposition, which further weakens the volatility and nonlinearity of the transportation carbon emission data series. Next, in order to explore the correlation between transportation carbon emission and its influencing factors, a double attention mechanism-optimized LSTM (DALSTM) model is constructed, in which a feature attention mechanism is added to the input side of the LSTM to highlight the key input features. Meanwhile, a temporal attention mechanism is added to the output side to extract the key historical moments. Finally, the SSA algorithm is improved by combining the Circle chaotic mapping, the dynamic inertia weight factor and the mixed variance operator strategies, ISSA-DALSTM models are established for each component separately, and the predicted values of each component are reconstructed. By measuring the carbon emission data of China's transportation industry from 1990 to 2019, it is found that the root mean square error, mean square error, and mean absolute percentage error of the proposed model are respectively 5.3088, 3.5661 and 0.4439, which are better than those of other comparative models, thus verifying the validity of the proposed model.

To address the nonlinear and volatile characteristics of carbon emission data sequences in the transportation industry, as well as the low prediction accuracy caused by the coupling of multiple influencing factors, this study develops a carbon emission prediction model that combines hybrid feature engineering (RF-MIC), improved variational mode decomposition (IVMD), the animated oat optimization algorithm (AOO), and bidirectional long short-term memory (Bi-LSTM). First, a hybrid feature selection method Based on random forest (RF) and the maximal information coefficient (MIC) is constructed to quantify the contribution of each factor, remove redundant disturbances, and identify the key drivers of carbon emissions. Second, a multi-objective decomposition framework based on variational mode decomposition (VMD) is constructed by using the escape optimization algorithm (ESC) and Pareto optimality to adaptively optimize the number of modes K and the penalty factor α. The original carbon emission sequence is then decomposed into a series of stationary modal components, thereby mitigating its nonlinearity and volatility. Third, an AOO-based BiLSTM hyperparameter optimization theory is established, where AOO is employed to globally optimize hyperparameters such as the number of hidden layer neurons and the learning rate of BiLSTM, preventing the model from falling into local optima. Finally, prediction sub-models based on AOO-BiLSTM are constructed for each modal component, and the predicted results of all components are integrated and reconstructed to obtain the final prediction value. The proposed model is validated using carbon emission data from China’s transportation industry from 1990 to 2023. The results show that, compared with the optimal benchmark model, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed model are reduced by 35.77%, 40.48%, and 59.52%, respectively, demonstrating its effectiveness in predicting carbon emissions in the transportation industry.

With the continual rise in the number of motor vehicles in urban areas, traffic congestion has become increasingly severe, adversely affecting environmental protection and urban operational efficiency. Consequently, it is of critical importance to accurately predict traffic congestion for traffic management and optimization. However, existing research still faces limitations in modeling the dynamic, time-varying characteristics of traffic flow and the complex interactions among road segments. To address these challenges, a gated spatiotemporal convolutional network model based on graph neural networks was proposed to more effectively capture and predict traffic congestion. Firstly, an improved K-means clustering algorithm was employed to divide the raw data into multiple congestion-state categories, which are then incorporated as auxiliary features to enhance feature representation. Next, a gated temporal convolutional network was introduced to capture the temporal properties and dynamic dependencies in traffic data, and a dynamic adaptive gated graph convolutional network was constructed to achieve feature fusion and dynamic weight allocation through a signal generation module and a dual-modulation mechanism, thereby facilitating effective extraction of spatiotemporal features. Finally, residual connections were incorporated to improve training stability, and skip connections were utilized to integrate multi-level and multi-scale features. Experimental results on real-world PeMS08 and PeMS04 datasets demonstrate that the proposed model achieves superior prediction accuracy compared with other baseline methods.