Wenjing Zhang
ABSTRACT
Traditional traffic prediction methods cannot effectively use many traffic data. Deep learning can mine the information behind big data. For example, recurrent neural network can effectively extract time rules. Convolution neural network can extract spatial features. And, graph convolution neural network is convenient for graph data processing, but it still has its limitations. At present, the hybrid method highlights its advantages in the field of transportation and realizes the complementary advantages of traditional methods and deep learning methods. On this basis, this paper summarizes the short-term traffic volume forecasting methods.
- Luntian Mou, Pengfei Zhao, Haitao Xie, and Yanyan Chen. T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction. IEEE Access, 2019, 7:98053-98060.Google Scholar
Cross Ref
- Xin Fu, Wei Luo, Chengyao Xu, and Xiaoxuan Zhao. Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit. Mathematical Problems in Engineering, 2020, 2020(12):1-13.Google Scholar
- Yuankai Wu, Huachun Tan, Lingqiao Qin, Bin Ran, and Zhuxi Jiang. A Hybrid Deep Learning Based Traffic Flow Prediction Method and Its Understanding. Transportation research, 2018, 90(MAY):166-180.Google Scholar
- Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. 2017.Google Scholar
- Sangsoo Lee, and Daniel B. Fambro. Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. Transportation Research Record Journal of the Transportation Research Board, 1999, 1678(1):179-188.Google Scholar
- Saiqun Lu, Qiyan Zhang, Guangsen Chen, and Dewen Seng. A Combined Method for Short-Term Traffic Flow Prediction Based on Recurrent Neural Network. AEJ - Alexandria Engineering Journal, 2020.Google Scholar
- Yuelei Xiao and Yang Yin. Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction. Information, 2019.Google Scholar
- Shuying Liu, Zhang Li, and Hangfu Li. Research on Short-Term Traffic Flow Prediction Model Based on RNN-LSTM, Proceedings of International Conference on AI and Big Data Application(AIBDA2019), 2019:109-116.Google Scholar
- Zheng Zhao, Weihai Chen, and Xingming Wu, Peter C. Y. Chen, Jingmeng Liu. LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast. IET Intelligent Transport Systems, 2017, 11(2):68-75.Google Scholar
- Xijun Zhang and Qirui Zhang. Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model. Computer Modeling in Engineering and Sciences, 2020, 125(1):95-109.Google ScholarCross Ref
- Guo Da, Xia Xingwen, Zhu Lin, and Zhang Yong. Dynamic Modification Neural Network Model for Short-term Traffic Prediction. Procedia Computer Science, 2021, 187.Google Scholar
- Yu Xiaojie, Sun Lijun, Yan Yang, and Liu Guanfeng. A Short-Term Traffic Flow Prediction Method Based on Spatial–Temporal Correlation Using Edge Computing. Computers and Electrical Engineering, 2021, 93.Google Scholar
- Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33:922-929.Google Scholar
- Kun Yu, Xizhong Qin, Zhenhong Jia, Yan Du, and Mengmeng Lin. Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction. Sensors, 2021, 21.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need, Advances in Neural Information Processing Systems. 2017: 5998-6008.Google Scholar
- Haifeng Zheng, Feng Lin, Xinxin Feng, and Youjia Chen. A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems, 2020, PP(99):1-11.Google Scholar
- Wang Ke, Ma Changxi, Qiao Yihuan, Lu Xijin, Hao Weining, and Dong Sheng. A Hybrid Deep Learning Model with 1DCNN-LSTM-Attention Networks for Short-Term Traffic Flow Prediction. Physica A: Statistical Mechanics and its Applications, 2021(12):126293.Google Scholar
- Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, and Kaixun Chen. A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting. Journal of Advanced Transportation, 2020, 2020(4):1-15.Google Scholar
- Ma Changxi, Tan Limin, and Xu Xuecai. Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing. Promet - Traffic&Transportation, 2020, 32(6).Google Scholar
- Qian Yongsheng, Zeng Junwei, Zhang Shanfu, Xu Dejie, and Wei Xuting. Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network. Tehnicki Vjesnik, 2020, 27(4):1270-1276.Google Scholar
- Zhang Rongji, Sun Feng, Song Ziwen, Wang Xiaolin, Du Yingcui, and Dong Shulong. Short-Term Traffic Flow Forecasting Model Based on GA-TCN. Journal of Advanced Transportation, 2021, 2021(722):1-13.Google Scholar
- Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. Computer Science, 2014.Google Scholar
- Zhan Aiyun, Du Fei, Chen Zhaozheng, Yin Guanxiang, Wang Meng, Zhang Yuejin, Jiang Nan, Riad Khaled, and Lin Weiwei. A traffic flow forecasting method based on the GA-SVR. Journal of High Speed Networks, 2022, 28(2).Google Scholar
- Wangdi Du, Qingyong Zhang, Yuepeng Chen, and Ziliu Ye. San Urban Short-term Traffic Flow Prediction Model Based on Wavelet Neural Network with Improved Whale Optimization Algorithm. Sustainable Cities and Society, 2021, 69(3):102858.Google ScholarCross Ref
- Yanni Peng and Wanli Xiang. Short-Term Traffic Volume Prediction Using GA-BP Based on Wavelet Denoising and Phase Space Reconstruction. Physica A: Statistical Mechanics and its Applications, 549.Google Scholar
- Bing Qichun, Shen Fuxin, Chen Xiufeng, Zhang Weijian, Hu Yanran, and Qu Dayi. A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model. Discrete Dynamics in Nature and Society, 2021.Google Scholar
- Junyi Li, Fang Guo, Arun Sivakumarb, Yanjie Dong, and Rajesh Krishnan. TransFerability Mprovement in Short-Term Traffic Prediction Using Stacked LSTM Network. Transportation Research Part C Emerging Technologies, 2021, 124:102977.Google ScholarCross Ref
- J. M. Bates and C. W. J. Granger. The Combination of Forecasts. Operational Research Quarterly, 1969Google Scholar
Index Terms
- A Survey of Short-Term Traffic Volume Prediction Methods Based on Composite Models
Index terms have been assigned to the content through auto-classification.
Recommendations
Application of temporal difference learning rules in short-term traffic flow prediction
Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent ...
Short-term load forecasting using lifting scheme and ARIMA models
Research highlights Short-term load forecasting is achieved using a lifting scheme and autoregressive integrated moving average (ARIMA) models. Lifting scheme is embedded into the ARIMA models to enhance forecasting accuracy. The Coeflet 12 wavelet is ...
Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network
ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information EngineeringEfficient and accurate traffic prediction is the premise of the development of autonomous driving technology. In-depth research is made on the issue of short-term traffic speed prediction in autonomous driving systems. In view of the time-varying ...
Comments