融合变分模态分解的时空卷积短时车速预测

doi:10.16182/j.issn1004731x.joss.22-0394

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (8): 1651-1660.doi: 10.16182/j.issn1004731x.joss.22-0394

融合变分模态分解的时空卷积短时车速预测

张凯(), 卢海鹏, 韩莹, 张龄允, 丁昱杰

南京信息工程大学自动化学院，江苏南京，210044

收稿日期:2022-04-21 修回日期:2022-06-29 出版日期:2023-08-25 发布日期:2023-08-15
作者简介:
张凯(1965-)，男，教授，博士，研究方向为智能出行。E-mail：zkark@nuist.edu.cn
基金资助:
国家自然科学基金(62076136)

Short-term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition

Kai Zhang(), Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding

School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2022-04-21 Revised:2022-06-29 Online:2023-08-25 Published:2023-08-15

摘要/Abstract

关键词: 短时车速预测, 卷积神经网络, 图卷积神经网络, 双向长短期记忆网络, 变分模态分解

Key words: short-term vehicle speed prediction, convolutional neural networks, graph convolutional neural networks, bidirectional long short time memory network, variational modal decomposition

张凯, 卢海鹏, 韩莹, 张龄允, 丁昱杰. 融合变分模态分解的时空卷积短时车速预测[J]. 系统仿真学报, 2023, 35(8): 1651-1660.

Kai Zhang, Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding. Short-term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition[J]. Journal of System Simulation, 2023, 35(8): 1651-1660.

图/表 10

图1

图2

表1

表2

表3

图3

图4

图5

图6

图7

参考文献 21

1	刘明宇, 吴建平, 王钰博, 等. 基于深度学习的交通流量预测[J]. 系统仿真学报, 2018, 30(11): 4100-4105, 4114.
	Liu Mingyu, Wu Jianping, Wang Yubo, et al. Traffic Flow Prediction Based on Deep Learning[J]. Journal of System Simulation, 2018, 30(11): 4100-4105, 4114.
2	Hassan T, El-Mowafy A, Wang Kan. A Review of System Integration and Current Integrity Monitoring Methods for Positioning in Intelligent Transport Systems[J]. IET Intelligent Transport Systems, 2021, 15(1): 43-60.
3	李伟, 周玉臣, 林圣琳, 等. 仿真模型验证方法综述[J]. 系统仿真学报, 2019, 31(7): 1249-1256.
	Li Wei, Zhou Yuchen, Lin Shenglin, et al. Review of Simulation Model Validation Methods[J]. Journal of System Simulation, 2019, 31(7): 1249-1256.
4	Shehu A, Van der Spoel David. Editorial Overview: Theory and Simulation and Their New Friends[J]. Current Opinion in Structural Biology, 2021, 67: iii-v.
5	Williams B M, Hoel L A. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results[J]. Journal of Transportation Engineering, 2003, 129(6): 664-672.
6	Yao Baozhen, Chen Chao, Cao Qingda, et al. Short-term Traffic Speed Prediction for an Urban Corridor[J]. Computer-aided Civil and Infrastructure Engineering, 2017, 32(2): 154-169.
7	程山英. 交通拥堵汽车流量准确预测仿真研究[J]. 计算机仿真, 2017, 34(10): 123-126.
	Cheng Shanying. Simulation Research on Traffic Flow Forecasting of Traffic Jam[J]. Computer Simulation, 2017, 34(10): 123-126.
8	李少伟, 陈永生. 城市轨道交通客流预测算法设计与仿真[J]. 计算机科学, 2014, 41(2): 276-279.
	Li Shaowei, Chen Yongsheng. Design and Simulation of Passenger Flow Forecast Algorithm for Urban Rail Transit[J]. Computer Science, 2014, 41(2): 276-279.
9	Qu Licheng, Jiao Lü, Li Wei, et al. Features Injected Recurrent Neural Networks for Short-term Traffic Speed Prediction[J]. Neurocomputing, 2021, 451: 290-304.
10	Yang Bailin, Sun Shulin, Li Jianyuan, et al. Traffic Flow Prediction Using LSTM With Feature Enhancement[J]. Neurocomputing, 2019, 332: 320-327.
11	夏容, 江官星. 基于LSTM的软件时间序列延迟预测仿真[J]. 计算机仿真, 2021, 38(12): 435-439.
	Xia Rong, Jiang Guanxing. Software Time Series Delay Prediction Simulation Based on LSTM[J]. Computer Simulation, 2021, 38(12): 435-439.
12	王祥雪, 许伦辉. 基于深度学习的短时交通流预测研究[J]. 交通运输系统工程与信息, 2018, 18(1): 81-88.
	Wang Xiangxue, Xu Lunhui. Short-term Traffic Flow Prediction Based on Deep Learning[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1): 81-88.
13	戢晓峰, 戈艺澄. 基于深度学习的节假日高速公路交通流预测方法[J]. 系统仿真学报, 2020, 32(6): 1164-1171.
	Ji Xiaofeng, Ge Yicheng. Holiday Highway Traffic Flow Prediction Method Based on Deep Learning[J]. Journal of System Simulation, 2020, 32(6): 1164-1171.
14	温惠英, 张东冉. 基于Bi-LSTM模型的高速公路交通量预测[J]. 公路工程, 2019, 44(6): 51-56.
	Wen Huiying, Zhang Dongran. Highway Traffic Volume Prediction Based on Bi-LSTM Model[J]. Highway Engineering, 2019, 44(6): 51-56.
15	王秋雯, 陈彦如, 刘媛春. 基于卷积长短时记忆神经网络的城市轨道交通短时客流预测[J]. 控制与决策, 2021, 36(11): 2760-2770.
	Wang Qiuwen, Chen Yanru, Liu Yuanchun. Metro Short-term Traffic Flow Prediction With ConvLSTM[J]. Control and Decision, 2021, 36(11): 2760-2770.
16	Li Tao, Ni Anning, Zhang Chunqin, et al. Short-term Traffic Congestion Prediction With Conv-BiLSTM Considering Spatio-temporal Features[J]. IET Intelligent Transport Systems, 2020, 14(14): 1978-1986.
17	Lu Zhilong, Weifeng Lü, Cao Yabin, et al. LSTM Variants Meet Graph Neural Networks for Road Speed Prediction[J]. Neurocomputing, 2020, 400: 34-45.
18	Zhao Ling, Song Yujiao, Zhang Chao, et al. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3848-3858.
19	丁恒, 郑小燕, 刘燕, 等. 基于小波分析的随机交通流组合预测方法研究[J]. 系统仿真学报, 2012, 24(2): 377-381.
	Ding Heng, Zheng Xiaoyan, Liu Yan, et al. Stochastic Traffic Series Time Combination Forecasting Based on Wavelet Analysis[J]. Journal of System Simulation, 2012, 24(2): 377-381.
20	Huang Haichao, Chen Jingya, Huo Xinting, et al. Effect of Multi-scale Decomposition on Performance of Neural Networks in Short-term Traffic Flow Prediction[J]. IEEE Access, 2021, 9: 50994-51004.
21	Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.

数据集	模型	RMSE	MAE	R ²
洛杉矶	ARIMA	6.743	5.929	0.810
	SVM	6.426	5.441	0.828
	VGBLSTM	3.356	2.264	0.952
广州	ARIMA	4.491	3.405	0.752
	SVM	4.151	3.125	0.788
	VGBLSTM	2.255	1.699	0.937

数据集	模型	RMSE	MAE	R ²
洛杉矶	VMD-BiLSTM	4.835	3.006	0.898
	GCN-BiLSTM	4.427	3.375	0.917
	VGBLSTM	3.356	2.264	0.952
广州	VMD-BiLSTM	2.865	2.227	0.899
	GCN-BiLSTM	3.178	2.484	0.876
	VGBLSTM	2.255	1.699	0.937

数据集	模型	RMSE	MAE	R ²
洛杉矶	BiLSTM	6.098	5.155	0.845
	CNN-BiLSTM	5.345	4.048	0.881
	TGCN	4.714	3.958	0.906
	VGBLSTM	3.356	2.264	0.952
广州	BiLSTM	3.984	3.097	0.801
	CNN-BiLSTM	3.757	2.939	0.826
	TGCN	3.429	2.684	0.855
	VGBLSTM	2.255	1.699	0.937

[1]	蔡兴泉, 李治均, 奚梦瑶, 孙海燕. 基于神经网络的手绘服饰图纹上色及风格迁移[J]. 系统仿真学报, 2023, 35(3): 604-615.
[2]	杨丰玮, 陈鹏, 郗凯, 蒲华林, 刘雪垠. 融合空间信息的运动想象脑电在线分类方法[J]. 系统仿真学报, 2023, 35(2): 254-267.
[3]	任彬, 汪小雨. 面向航天测发任务的动作识别与追踪研究[J]. 系统仿真学报, 2022, 34(8): 1674-1681.
[4]	邱俊杰, 郑红, 程云辉. 基于多尺度LSTM预测模型研究[J]. 系统仿真学报, 2022, 34(7): 1593-1604.
[5]	张立峰, 王会忍. 基于卷积神经网络及有限元仿真的电容层析成像图像重建[J]. 系统仿真学报, 2022, 34(4): 712-718.
[6]	敖邦乾, 杨莎, 令狐金卿, 叶振环. 基于级联神经网络疲劳驾驶检测系统设计[J]. 系统仿真学报, 2022, 34(2): 323-333.
[7]	王亚茹, 杨凯, 翟永杰, 郭聪彬, 赵文清, 苏杰. 基于人工图像数据扩充的输电线路绝缘子识别[J]. 系统仿真学报, 2022, 34(11): 2337-2347.
[8]	徐佳乐, 张海东, 赵东海, 倪晚成. 基于卷积神经网络的陆战兵棋战术机动策略学习[J]. 系统仿真学报, 2022, 34(10): 2181-2193.
[9]	郭业才, 王庆伟. 基于截断迁移与并行残差网络的调制识别算法[J]. 系统仿真学报, 2022, 34(09): 2009-2018.
[10]	赵瑛, 陆耀, 张健, 梁启弟, 龙炜. 基于深度神经网络的多视角人体动作识别[J]. 系统仿真学报, 2021, 33(5): 1019-1030.
[11]	仝卫国, 庞雪纯, 朱赓宏. 基于卷积神经网络的气液两相流流型识别方法[J]. 系统仿真学报, 2021, 33(4): 883-891.
[12]	马榕, 陈秋瑞, 张晗, 梅铮, 王锐, 魏伟. 基于单目深度估计的低功耗视觉里程计[J]. 系统仿真学报, 2021, 33(12): 3001-3011.
[13]	黄欣, 方钰, 顾梦丹. 基于卷积神经网络的X线胸片疾病分类研究[J]. 系统仿真学报, 2020, 32(6): 1188-1194.
[14]	樊名鲁, 王艳, 纪志成. 基于特征生成的轴承不均衡数据故障诊断[J]. 系统仿真学报, 2020, 32(12): 2438-2448.
[15]	王红军, 黎邹邹, 邹湘军. 基于Adaboost与CNN的木材表面缺陷检测[J]. 系统仿真学报, 2019, 31(8): 1636-1645.

融合变分模态分解的时空卷积短时车速预测

Short-term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价