A Method for Predicting Carbon Emission of Railway Transportation System Based on an LSTM Network with Dynamic Input via Sliding Window

Low-carbon development of railway is significant for the entire transportation system to achieve the goals of carbon peaking and carbon neutrality. Currently, there are a few studies on the methods for predicting carbon emission of railway transportation system, and their prediction accuracy is, in general, low. To improve the accuracy of corresponding prediction methods, considering the relationship between the historical and present information in the carbon emission time series data, a sliding window algorithm is integrated into a long short-term memory (LSTM) network to develop a prediction model for railway transportation system. A Grey Relation Analysis method is used to select the key factors with a higher correlation. The data highly correlated with the key factors identified are used as the input variables of the prediction model to improve the accuracy of the LSTM network. In addition, it is found that, by integrating a sliding window, the input of the network has been significantly improved. To study the impacts of future emission reduction policies on carbon emissions of railway transportation, the prediction model is used to analyze various policies under different scenarios. A polynomial error fitting method is used for error correction to improve the model accuracy. The data on carbon emissions from railway transportation from 1980 to 2019 are taken as the case study. Six key factors are identified and then selected from seventeen influencing factors of railway carbon emission that are reported in the literature, by using a Grey Relation Analysis. Then selected data is segmented into subsequences by the sliding window. The prediction accuracy under different window lengths is compared to select the optimal window parameters for the improved LSTM model. The improved LSTM model obtained is then compared with the original LSTM, BPNN, and RNN models. Study results show that the improved LSTM model reduces the average relative error to 0.392%, while that of the original LSTM model is 3.862%, the BPNN model 1.535%, and the RNN model 0.760%. Compared to these traditional models, the improved LSTM model consistently presents a higher accuracy. According to historical trends and development policies, a baseline scenario and three future emission reduction scenarios are set. The improved LSTM model is used to predict the carbon emissions of railway transportation in the next decade. Under the four scenarios, the carbon emissions of railway transportation in 2030 is 9.83×106 t, 8.91×106 t, 8.62×106 t, and 8.09×106 t, respectively. In summary, the improved LSTM model with sliding window can further improve the prediction accuracy of carbon emissions for railway transportation, and the scenario analysis based on various policy assumptions can provide a feasible path for future low-carbon development of railway transportation.