Citation: | ZHAO Zhenxing, ZENG Wei, TANG Chenjia. A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship[J]. Journal of Transport Information and Safety, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015 |
[1] |
申雷霄, 陆宇航, 郭建华. 卡尔曼滤波短时交通流预测普通国省道适应性研究[J]. 交通信息与安全, 2021, 39(5): 117-127. doi: 10.3963/j.jssn.1674-4861.2021.05.015
SHEN L X, LU Y H, GUO J H. Adaptability of Kalman filter for short-time traffic flow forecasting on national and provincial highways[J]. Journal of Transport Information and Safety, 2021, 39(5): 117-127(. in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.05.015
|
[2] |
何祖杰, 吴新烨, 刘中华. 基于改进灰狼算法优化支持向量机的短期交通流预测[J]. 厦门大学学报(自然科学版), 2022, 61(2): 288-297. https://www.cnki.com.cn/Article/CJFDTOTAL-XDZK202202019.htm
HE Z J, WU X Y, LIU Z H. Optimized SVM model for short-term traffic flow prediction based on improved gray wolf optimizer[J]. Journal of Xiamen University(Natural Science Edition), 2022, 61(2): 288-297(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDZK202202019.htm
|
[3] |
童林, 官铮. 改进鲸鱼优化支持向量机的交通流量模糊粒化预测[J]. 计算机应用, 2021, 41(10): 2919-2927. doi: 10.11772/j.issn.1001-9081.2020122048
TONG L, GUAN Z. Fuzzy granulation prediction of traffic flow based on improved whale optimization support vector machine[J]. Journal of Computer Applications, 2021, 41(10): 2919-2927(. in Chinese) doi: 10.11772/j.issn.1001-9081.2020122048
|
[4] |
龚勃文, 林赐云, 李静, 等. 基于核自组织映射—前馈神经网络的交通流短时预测[J]. 吉林大学学报(工学版), 2011, 41(4): 938-943. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201104008.htm
GONG B W, LIN C Y, LI J, et al. Short-term traffic flow prediction based on KSOM-BP neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(4): 938-943(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201104008.htm
|
[5] |
冯金巧, 杨兆升, 孙占全, 等. 基于小波分析的交通参数组合预测方法[J]. 吉林大学学报(工学版), 2010, 40(5): 1220-1224. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201005011.htm
FENG J Q, YANG Z S, SUN Z Q, et al. Combined method for traffic parameter prediction based on wavelet analysis[J]. Journal of Jilin University(Engineering and Technology Edition), 2010, 40(5): 1220-1224(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201005011.htm
|
[6] |
曹洁, 张敏, 张红等. 基于IFA优化RBF神经网络的短时交通流预测模型[J]. 兰州理工大学学报, 2022, 48(4): 99-104. https://www.cnki.com.cn/Article/CJFDTOTAL-GSGY202204015.htm
CAO J, ZHANG M, ZHANG H, et al. Short-term traffic flow prediction model based on IFA optimized RBF neural network[J]. Journal of Lanzhou University of Technology, 2022, 48(4): 99-104(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GSGY202204015.htm
|
[7] |
赵庶旭, 崔方. 一种改进的深度置信网络在交通流预测中的应用[J]. 计算机应用研究, 2019, 36(3): 772-775, 785. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201903027.htm
ZHAO S X, CUI F. Application of improved deep belief network in traffic flow forecasting[J]. Application Research of Computers, 2019, 36(3): 772-775, 785(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201903027.htm
|
[8] |
陈宇, 王炜, 华雪东, 等. 基于递归框架的高速公路交通流量实时预测方法[J]. 交通信息与安全, 2023, 41(1): 124-131. doi: 10.3963/j.jssn.1674-4861.2023.01.013
CHEN Y, WANG W, HUA X D, et al. A recursive framework-based approach for real-time traffic flow forecasting for highways[J]. Journal of Transport Information and Safety, 2023, 41(1): 124-131(. in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.01.013
|
[9] |
张玺君, 陶冶, 张冠男, 等. 基于ACapsGRU的短时交通流预测研究[J]. 华中科技大学学报(自然科学版), 2022, 50(4): 51-56. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG202204009.htm
ZHANG X J, TAO Y, ZHANG G N, et al. Research on short-term traffic flow forecast based on ACapsGRU[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2022, 50(4): 51-56(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG202204009.htm
|
[10] |
MA X, DAI Z, HE Z, et al. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction[J]. Sensors, 2017, 17(4): 818.
|
[11] |
袁华, 陈泽濠. 基于时间卷积神经网络的短时交通流预测算法[J]. 华南理工大学学报(自然科学版), 2020, 48(11): 107-113, 122. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202011013.htm
YUAN H, CHEN Z H. Short-term traffic flow prediction based on temporal convolutional networks[J]. Journal of South China University of Technology(Natural Science Edition), 2020, 48(11): 107-113, 122(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202011013.htm
|
[12] |
ZHAO L, SONG Y, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858.
|
[13] |
BAI J, ZHU J, SONG Y, et al. A3T-GCN: attention temporal graph convolutional network for traffic forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 10(7): 485.
|
[14] |
CHEN L, SHAO W, LYU M, et al. AARGNN: An attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17201-17211.
|
[15] |
YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]. The 27th International Joint Conference on Artificial Intelligence, Freiburg, GER: IJCAI, 2018.
|
[16] |
GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]. 33th AAAI Conference on Artificial Intelligence, Palo Alto, USA: AAAI, 2019.
|
[17] |
SONG C, LIN Y, GUO S, et al. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting[C]. 34th AAAI Conference On Artificial Intelligence, Palo Alto, USA: AAAI, 2020.
|
[18] |
YE J, ZHAO J, YE K, et al. How to build a graph-based deep learning architecture in traffic domain: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(5): 3904-3924.
|
[19] |
JIA T, YAN P. Predicting citywide road traffic flow using deep spatiotemporal neural networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(5): 3101-3111.
|
[20] |
刘宜成, 李志鹏, 吕淳朴, 等. 基于动态时间调整的时空图卷积路网交通流量预测研究[J]. 交通运输系统工程与信息, 2022, 22(3): 147-157, 178. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203017.htm
LIU Y C, LI Z P, LYU C P, et al. Network-wide traffic flow prediction research based on DTW algorithm spatial-temporal graph convolution[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 147-157, 178(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203017.htm
|
[21] |
甘萍, 农丽萍, 张文辉, 等. 一种用于交通预测的注意力时空图神经网络[J]. 西安电子科技大学学报, 2023, 50(1): 168-176. https://www.cnki.com.cn/Article/CJFDTOTAL-XDKD202301019.htm
GAN P, NONG L P, ZHANG W H, et al. Attention spatial-temporal graph neural network for traffic prediction[J]. Journal of Xidian University, 2023, 50(1): 168-176(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDKD202301019.htm
|
[22] |
杨兴锐, 赵寿为, 张如学, 等. 结合自注意力和残差的BiLSTM_CNN文本分类模型[J]. 计算机工程与应用, 2022, 58(3): 172-180. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202203015.htm
YANG X R, ZHAO S W, ZHANG R X, et al. BiLSTM_CNN classfication model based on self-attention and residual network[J]. Computer Engineering and Applications, 2022, 58(3): 172-180(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202203015.htm
|
[23] |
刘文星. 网络攻击频率混沌时间序列预测[D]. 长沙: 国防科学技术大学, 2008.
LIU W X. Prediction of network attack frequency based on chaotic time series[D]. Changsha: National University of Defense Technology, 2008(. in Chinese)
|