An improved sparrow search algorithm and BiLSTM-Multi Head Attention neural network for predicting traffic carbon emission

Abstract

Accurate prediction of transportation carbon emissions is crucial for the government to formulate transportation strategies. However, due to the characteristics of the traffic carbon emission data, such as nonlinearity, uncertainty, and abrupt changes, accurately predicting traffic carbon emissions faces significant challenges. In this paper, we propose the LNSSA-BiLSTM-MHA model, which combines the improved Sparrow Search Algorithm based on the logistic-tent-cosine chaotic mapping and the Narrowed Opposition-Based Learning Strategy (LNSSA) with the BiLSTM-MHA model to improve the accuracy of transportation carbon emission prediction. Firstly, the LNSSA algorithm is proposed for model hyperparameter search, which combines Logistic-Tent-Cosine chaotic mapping with Narrowed Opposition-Based Learning strategy to improve the SSA algorithm. Among them, the logistic-tent-cosine chaotic mapping is used to uniformly initialize the population, and the Narrowed Opposition-Based Learning strategy is used to reduce the solution space. Secondly, the BiLSTM model is used to analyze the temporal data features in both forward and reverse directions. Finally, we combine the multihead attention mechanism to learn data features from different subspaces. The private data set of six intersections of roads in Xiangyang City from 2023 to 2024 is collected. We perform model training and experimental analysis on this private dataset. Using the evaluation metrics RMSE, MSE, and MAE, the results showed that the LNSSA-BiLSTM-MHA model outperformed the comparison models LSTM, GRU, LSTM-Attention, GRU-Attention, CNN-LSTM-Attention, CNN-GRU-Attention and ResNet-GRU-Attention by 32%, 54% and 35%, respectively, in six datasets.