Traffic Flow Prediction Model Based on Spatio-Temporal Feature Distillation Variational Autoencoder

OUYANG Yi; TANG Wen-yan; LI Yan-ling

doi:10.12263/DZXB.20230230

您当前的位置：

首页 >

文章列表页 >

Traffic Flow Prediction Model Based on Spatio-Temporal Feature Distillation Variational Autoencoder

PAPERS | 更新时间：2025-12-11

- Traffic Flow Prediction Model Based on Spatio-Temporal Feature Distillation Variational Autoencoder
- ACTA ELECTRONICA SINICA Vol. 52, Issue 6, Pages: 1938-1944(2024)
- 作者机构：
  
  浙江工商大学管理科学与电子商务学院，浙江杭州 310000
- 作者简介：
- 基金信息：
  
  Zhejiang Gongshang University “Digital+Discipline Construction Project”(SZJ2022C004);Zhejiang Gongshang University 2023 Provincial-level Teaching Platform Teaching Project(1310XJ0521036)
- DOI：10.12263/DZXB.20230230
  CLC： TN911.7;U491.1
- Received：10 March 2023，
  
  Revised：2023-12-06，
  
  Published：25 June 2024
- 稿件说明：
移动端阅览
欧阳毅,汤文燕,黎晏伶.基于特征蒸馏的变分编码器交通流预测模型[J].电子学报,2024,52(06):1938-1944.

OUYANG Yi, TANG Wen-yan, LI Yan-ling.Traffic Flow Prediction Model Based on Spatio-Temporal Feature Distillation Variational Autoencoder[J].Acta Electronica Sinica, 2024, 52(06): 1938-1944.
欧阳毅,汤文燕,黎晏伶.基于特征蒸馏的变分编码器交通流预测模型[J].电子学报,2024,52(06):1938-1944. DOI：10.12263/DZXB.20230230

OUYANG Yi, TANG Wen-yan, LI Yan-ling.Traffic Flow Prediction Model Based on Spatio-Temporal Feature Distillation Variational Autoencoder[J].Acta Electronica Sinica, 2024, 52(06): 1938-1944. DOI：10.12263/DZXB.20230230

摘要

针对交通流数据高维非线性和时空依赖性复杂，本文构建了基于特征蒸馏的变分贝叶斯编码器交通流预测模型.对每段时间序列对应的时间窗口特征，构建了基于多模态时间槽和空间槽的交通流特征提取模型.以时空槽特征提取模型作为特征知识蒸馏架构的输入.通过知识蒸馏结构提取的时空特征结晶体，利用教师模型指导学生模型的学习过程，从而提高学生模型的泛化能力.变分贝叶斯编码器对交通流时空特征结晶编码获取交通流数据的隐变量，根据隐变量的生成采样，利用解码器将其解码重构成新的预测值.实验结果表明，本文提出的模型预测性能显著提升，且中长期预测中鲁棒性更优.

Abstract

To improve the accuracy of traffic flow prediction and to solve the problems of high-dimensional nonlinearity and spatio-temporal dependence of traffic flow

a combined feature distillation and variational Bayes encoders traffic flow forecasting model (ST-DVBE) is proposed. First

to extract the time window characteristics corresponding to each time series

the multi-modal time slots and spatial slots are constructed. Second

with spatio-temporal slot feature extraction model as the input of feature knowledge distillation architecture

and space-time feature crystallization extracted by knowledge distillation structure

the learning process of student model is guided by teacher model

so as to improve the generalization ability of student model. Finally

the variational Bayesian encoder is employed to capture the latent variables of traffic flow data by encoding the crystallization of spatiotemporal features. Utilizing the generated latent variables

the decoder reconstructs them into new predicted values. Experimental results demonstrate a significant enhancement in predictive performance with the proposed model

especially with better robustness in mid- and long-term forecasting.

关键词

Keywords

references

马君 , 刘小冬 , 孟颖 . 基于神经网络的城市交通流预测研究‍ [J ] . 电子学报 , 2009 , 37 ( 5 ): 1092 - 1094 .

MA J , LIU X D , MENG Y . Research of urban traffic flow forecasting based on neural network [J ] . Acta Electronica Sinica , 2009 , 37 ( 5 ): 1092 - 1094 . (in Chinese)

MAKRIDAKIS S , HIBON M . ARMA models and the box-Jenkins methodology [J ] . Journal of Forecasting , 1997 , 16 ( 3 ): 147 - 163 .

MIRCHANDANI P , HEAD L . A real-time traffic signal control system: Architecture, algorithms, and analysis [J ] . Transportation Research Part C: Emerging Technologies , 2001 , 9 ( 6 ): 415 - 432 .

LESHEM G , RITOV Y . Traffic flow prediction using adaboost algorithm with random forests as a weak learner [J ] . International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering , 2007 , 1 ( 1 ): 1 - 6 .

TANG J , CHEN X , HU Z , et al . Traffic flow prediction based on combination of support vector machine and data denoising schemes [J ] . Physica A: Statistical Mechanics and Its Applications , 2019 , 534 : 120642 .

金苍宏 , 董腾然 , 陈天翼 , 等 . 融合序列分解与时空卷积的时序预测算法 [J ] . 电子学报 , 2021 , 49 ( 2 ): 233 - 238 .

JIN C H , DONG T R , CHEN T Y , et al . Spatio-temporal convolutional forecasting based on time-series decomposition strategy [J ] . Acta Electronica Sinica , 2021 , 49 ( 2 ): 233 - 238 . (in Chinese)

徐兴荣 , 刘聪 , 李婷 , 等 . 基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法 [J ] . 电子学报 , 2022 , 50 ( 8 ): 1975 - 1984 .

XU X R , LIU C , LI T , et al . Business process remaining time prediction: An approach based on bidirectional quasi recurrent neural network with attention [J ] . Acta Electronica Sinica , 2022 , 50 ( 8 ): 1975 - 1984 . (in Chinese)

LU H , GE Z , SONG Y , et al . A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting [J ] . Neurocomputing , 2021 , 427 : 169 - 178 .

HUSSAIN B , AFZAL M K , AHMAD S , et al . Intelligent traffic flow prediction using optimized GRU model [J ] . IEEE Access , 2021 , 9 : 100736 - 100746 .

HINTON G , VINYALS O , DEAN J . Distilling the knowledge in a neural network [EB/OL ] . [2023 ] . http://arxiv.org/abs/1503.02531.pdf http://arxiv.org/abs/1503.02531.pdf .

钱亚冠 , 马骏 , 何念念 , 等 . 面向边缘智能的两阶段对抗知识迁移方法 [J ] . 软件学报 , 2022 , 33 ( 12 ): 4504 - 4516 .

QIAN Y G , MA J , HE N N , et al . Two-stage adversarial knowledge transfer method for edge intelligence [J ] . Journal of Software , 2022 , 33 ( 12 ): 4504 - 4516 . (in Chinese)

LIU Y , ZHANG W , WANG J . Adaptive multi-teacher multi-level knowledge distillation [J ] . Neurocomputing , 2020 , 415 : 106 - 113 .

DAI W , HOU L , SHANG L , et al . Enabling multimodal generation on CLIP via vision-language knowledge distillation [C ] // Findings of the Association for Computational Linguistics: ACL 2022 . Stroudsburg : ACL , 2022 : 2383 - 2395 .

HUANG Y , HAO Y , XU J , et al . Compressing speaker extraction model with ultra-low precision quantization and knowledge distillation [J ] . Neural Networks , 2022 , 154 : 13 - 21 .

KINGMA D P , WELLING M . Auto-encoding variational Bayes [C ] // International Conference on Learning Representations (ICLR2014) . Canada : ICLR . 2014 : 1 - 14 .

YU B , YIN H , ZHU Z . Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting [C ] // Proceedings of the 27th International Joint Conference on Artificial Intelligence . New York : ACM , 2018 : 3634 - 3640 .

LI Y , YU R , SHAHABI C , et al . Diffusion convolutional recurrent neural network: Data-driven traffic forecasting ‍ [EB/OL ] . [2023 ] . http://arxiv.org/abs/1707.01926.pdf http://arxiv.org/abs/1707.01926.pdf .

ZHENG C , FAN X , WANG C , et al . GMAN: A graph multi-attention network for traffic prediction [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 1 ): 1234 - 1241 .

YOO B , LEE J , JU J , et al . Conditional temporal neural processes with covariance loss [C ] // International Conference on Machine Learning . PMLR , 2021 : 12051 - 12061 .

XU M , DAI W , LIU C , et al . Spatial-temporal transformer networks for traffic flow forecasting [EB/OL ] . [2023 ] . http://arxiv.org/abs/2001.02908.pdf http://arxiv.org/abs/2001.02908.pdf .

LI Z , LI W , HWANG K . Adaptive graph convolution networks for traffic flow forecasting [EB/OL ] . [2023 ] . http://arxiv.org/abs/2307.05517.pdf http://arxiv.org/abs/2307.05517.pdf .

FENG A , TASSIULAS L . Adaptive graph spatial-temporal transformer network for traffic flow forecasting [EB/OL ] . [2023 ] . http://arxiv.org/abs/2207.05064.pdf http://arxiv.org/abs/2207.05064.pdf .

WILLIAMS B M , DURVASULA P K , BROWN D E . Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models [J ] . Transportation Research Record , 1998 , 1644 ( 1 ): 132 - 141 .

LIU B , TANG X , et al . Traffic flow combination forecasting method based on improved LSTM and ARIMA [J ] . International Journal of Embedded Systems , 2020 , 12 ( 1 ): 22 - 30 .

BAI L , YAO L , KANHERE S , et al . Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting [C ] // Proceedings of the 28th International Joint Conference on Artificial Intelligence . New York : ACM , 2019 : 1981 - 1987 .

BAI L , YAO L , LI C , et al . Adaptive graph convolutional recurrent network for traffic forecasting [J ] . Advances in Neural Information Processing Systems , 2020 , 33 ( 1 ): 17804 - 17815 .

WU Z , PAN S , LONG G , et al . Graph wavenet for deep spatial-temporal graph modeling [C ] // Proceedings of the 28th International Joint Conference on Artificial Intelligence . New York : ACM , 2019 : 1907 - 1913 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model

Diverse Image Captioning via Conditional Variational Inference and Introspective Adversarial Learning

Distributed Fusion Target Tracking Based on Variational Bayes

Joint Carrier Frequency Offset and Channel Tracking for MU-MIMO-OFDM Uplink Systems

Variational Learning for Finite Beta-Liouville Mixture Models and Its Application

Related Author

LIAO Lei-yao

LIU Hao

LIU Ming-ming

LI Sui

LIU Bing

HU Zhen-tao

YANG Shi-bo

HU Yu-mei

Related Institution

National Key Laboratory of Radar Signal Processing, Xidian University

College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications

Mine Digitization Engineering Research Center of the Ministry of Education

School of Computer Science and Technology， China University of Mining and Technology

School of Artificial Intelligence,Henan University

⁰