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

肇晓楠; 谢新连; 赵瑞嘉

doi:10.3963/j.jssn.1674-4861.2023.01.018

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

doi: 10.3963/j.jssn.1674-4861.2023.01.018

大连海事大学综合运输研究所辽宁大连 116026

基金项目:

国家重点研发计划资助项目 2017YFC0805309

国家自然科学基金项目 72204035

详细信息

作者简介:
肇晓楠（1997—），硕士研究生.研究方向：交通运输规划与管理. E-mail：596894782@qq.com

通讯作者:
谢新连（1956—），博士，教授.研究方向：交通运输规划与管理.E-mail：xxlian@dlmu.edu.cn

中图分类号: U29;F532
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出版历程
- 收稿日期: 2022-04-26
- 网络出版日期: 2023-05-13

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

Integrated Transport Institute, Dalian Maritime University, Dalian 116026, Liaoning China

摘要

摘要: 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%，而原LSTM模型为3.862%，BPNN模型为1.535%，RNN模型为0.760%，即改进LSTM模型具有更高预测准确性；根据历史趋势和发展政策设置基准情景和3种未来减排情景，利用改进LSTM模型预测未来10年铁路运输碳排放量，在4种模拟情景下，铁路运输2030年的碳排放量分别为9.83×106 t、8.91×106 t、8.62×106 t和8.09×106 t。综上所述，引入滑动窗口的改进LSTM模型能进一步提高铁路运输碳排放量预测准确性，融合动态政策的情景分析可为未来铁路运输低碳发展提供可行路径。
- 铁路运输 /
- 碳排放 /
- 碳排放预测 /
- LSTM网络 /
- 滑动窗口
Abstract: 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.
- railway transportation /
- carbon emission /
- carbon emission prediction /
- long short-term memory network /
- sliding window

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图 1 LSTM结构图

Figure 1. LSTM structure diagram

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图 2 预测流程图

Figure 2. Forecast flow chart

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图 3 滑动时间窗口示意图

Figure 3. Schematic diagram of sliding window

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图 4 LSTM模型结构图

Figure 4. Structure diagram of LSTM model

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图 5 铁路碳排放与其影响因素相关度

Figure 5. Correlation between railway carbon emission and its influencing factors

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图 6 加入滑动时间窗前后及不同步长精度对比

Figure 6. Comparison of accuracy before and after adding sliding time window and different steps

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图 7 LSTM模型预测值与真实值对比

Figure 7. Comparison between predicted value and real value of LSTM model

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图 8 不同模型的准确度对比

Figure 8. Accuracy comparison of different models

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图 9 预测值对比实验结果

Figure 9. The predicted values are compared with the experimental result

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图 10 4种情景模式下铁路运输碳排放预测值

Figure 10. Carbon emission prediction of railway transportation under four scenarios

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表 1 铁路碳排放影响因素数据

Table 1. Data on Influencing Factors of railway carbon emission

年份	碳排放量/×10⁴t	GDP增速	电气化率/%	复线率/%	换算总周转量/×108km	能源消耗强度/（t·10^-4元）	总人口/×10⁴人	固定资产投资/（×10⁸元）	列车平均车速/（km/h）
1980	2 244.75	0.12	3.3	15.7	7 100	30.12	98 705
1981	2 215.82	0.08	3.3	16.5	7 185	29.24	100 072
1982	2 186.89	0.09	3.4	17.1	7 695	26.57	101 654
1983	2 157.96	0.12	4.2	17.8	8 422	23.15	103 008
1984	2 129.03	0.21	5.5	18.7	9 294	18.83	104 357
1985	2 100.10	0.25	8.0	19.2	10 542	15.43	105 851		40.0
1986	2 071.17	0.14	8.4	20.2	11 352	13.61	107 507		40.0
1987	2 042.24	0.17	9.4	21.3	12 315	12.32	109 300		40.0
1988	2 013.31	0.25	10.9	22.3	13 138	11.73	111 026		40.0
1989	1 984.39	0.13	12.0	23.6	13 407	10.16	112 704		40.0
1990	1 955.46	0.10	13.0	24.4	1 3211	7.62	114 333		40.0
1991	1 926.52	0.17	14.6	25.0	13 772	6.96	115 823		40.0
1992	1 897.59	0.24	15.8	25.5	14 696	6.45	117 171		40.0
1993	1 868.66	0.31	16.6	26.6	15 402	5.88	118 517		48.1
1994	1 839.73	0.36	16.6	28.7	16 059	5.29	119 850		48.1
1995	1 810.59	0.26	17.8	31.0	16 378	4.83	121 121		48.1
1996	1 771.82	0.17	17.8	32.5	16 243	4.11	122 389		48.1
1997	1 759.86	0.11	20.9	33.1	16 589	3.64	123 626		54.9
1998	1 734.48	0.07	22.5	34.2	15 952	3.26	124 761	562.42	55.2
1999	1 695.72	0.06	24.2	36.1	16 624	2.96	125 786	566.46	55.2
2000	1 657.35	0.11	25.3	36.5	17 750	2.64	126 743	517.40	61.6
2001	1 638.28	0.11	28.6	38.3	18 886	2.40	127 627	509.30	61.6
2002	1 592.80	0.10	29.2	38.7	19 881	2.16	128 453	623.52	61.6
2003	1 569.94	0.13	29.9	39.2	21 098	1.86	129 227	482.54	61.6
2004	1 530.35	0.17	30.4	39.1	23 797	1.49	129 988	516.32	65.7
2005	1 505.80	0.16	31.2	39.4	25 366	1.29	130 756	1 364.31	65.7
2006	1 103.55	0.17	37.0	39.8	26 910	0.82	131 448	2 075.97	65.7
2007	1 123.15	0.23	37.8	40.5	29 008	0.76	132 129	1 789.99	70.18
2008	1 134.92	0.18	39.1	41.6	31 388	0.69	132 802	3 375.54	70.18
2009	1 070.36	0.09	46.2	43.8	31 490	0.59	133 450	7 875.60	200.0
2010	1 163.99	0.18	49.4	44.8	36 406	0.59	134 091	8 426.52	200.0
2011	1 187.58	0.18	52.0	45.2	39 078	0.56	134 735	4 610.84	200.0
2012	1 174.44	0.10	53.5	46.2	38 999	0.43	135 404	6 339.67	200.0
2013	1 167.81	0.10	54.0	47.8	39 769	0.32	136 072	6 657.45	200.0
2014	1 107.24	0.09	58.0	48.6	39 134	0.31	136 782	8 088.00	200.0
2015	1 051.54	0.07	61.2	53.5	35 714	0.30	137 462	8 238.00	200.0
2016	1 066.37	0.08	64.7	54.9	36 371	0.30	138 271	8 015.00	200.0
2017	1 089.94	0.11	64.8	56.5	40 419	0.26	139 008	8 010.00	200.0
2018	1 088.22	0.10	71.9	59.0	43 074	0.23	139 538	8 028.00	200.0
2019	1 095.30	0.08	72.8	59.5	44 888	0.20	140 005	7 819.00	200.0
注：数据来源于《铁道统计公报》《中国统计年鉴》《中国能源统计年鉴》。

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表 2 各因素与碳排放间关联度

Table 2. Correlation between various factors and carbon emission

名称	关联度
GDP增速	0.948
电气化率	0.906
复线率	0.980
换算总周转量	0.969
能源消耗强度	0.993
人口规模	0.963
固定资产投资额	0.590
平均列车时速	0.852

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表 3 测试集预测值及评价指标

Table 3. Predicted value and evaluation index of test set

年份	真实值	预测值	相对误差	平均误差
2016	1 066.370	1 063.856	0.002 35	0.003 92
2017	1 089.940	1 086.284	0.003 35
2018	1 088.220	1 083.276	0.004 54
2019	1 095.300	1 089.344	0.005 43

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表 4 对比试验相对误差值

Table 4. Comparative test relative error value

年份	真实值	LSTM		BPNN		RNN		Original LSTM
年份	真实值	预测值	误差	预测值	误差	预测值	误差	预测值	误差
2016	1 066.37	1 063.856	0.002 35	1 100.017	0.031 55	1 071.493	0.004 80	1 022.777	0.040 88
2017	1 089.94	1 086.284	0.003 35	1 103.280	0.012 23	1 081.811	0.007 45	1 130.951	0.037 63
2018	1 088.22	1 083.276	0.004 54	1 099.085	0.009 98	1 079.667	0.007 86	1 047.345	0.037 56
2019	1 095.30	1 089.344	0.005 43	1 086.927	0.007 64	1 084.019	0.010 30	1 137.381	0.038 42
平均误差			0.003 91		0.015 35		0.007 60		0.038 62

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表 5 2020—2030年各因素发展速率

Table 5. Development rate of various factors from 2020 to 2030 单位: %

变化率	GDP增速	电气化率	复线率	换算总周转量	能源消耗强度	人口规模
中	2	4	4	3	-8	0.05
强	1	6	6	2	-10	0.01

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表 6 铁路碳排放的情景模式

Table 6. Scenario of railway carbon emission

情景模式	GDP增速	电气化率	复线率	换算总周转量	能源消耗强度	人口规模
基准模式	中	中	中	中	中	中
节能模式	中	强	强	中	中	中
绿色模式	强	中	中	强	强	强
低碳模式	强	强	强	强	强	强

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