基于滑动窗口动态输入LSTM网络的铁路运输系统碳排放量预测方法

铁路运输的低碳发展对交通系统实现“双碳”战略目标有着重要意义。针对当前铁路运输碳排放预测研究较少、预测精度不高的问题，考虑碳排放时间序列数据中历史信息和当前信息间的相关性，引入滑动窗口，结合长短期记忆（LSTM）网络，构建铁路运输碳排放量预测模型。采用灰色关联分析法计算铁路运输碳排放量各影响因素的关联度值，筛选铁路运输碳排放量的关键影响因素，使用高关联性数据作为预测模型的输入变量，提高预测精度；应用LSTM网络为基础预测模型，通过引入滑动窗口改进神经网络的数据输入;考虑未来减排政策变化对铁路运输碳排放量的影响，融合基于动态政策的情景分析，构建铁路碳排放预测模型，并利用多项式误差拟合方法进行误差修正，提高预测结果准确性。以1980—2019年铁路运输碳排放相关数据为例，从现有文献中总结出17个铁路碳排放影响因素，利用灰色关联分析法从中筛选出6个关键因素，通过滑动窗口对筛选出的数据进行子序列分割，测试不同长度窗口下的预测精度，选择最优窗口参数，建立改进LSTM模型进行预测，并将预测结果与原LSTM、BPNN和RNN模型进行对比，结果表明：改进LSTM模型将相对误差平均值降低至0.392%，...

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.