Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition
Source abstract
Abstract
As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Funding
National key R&D Program of China
References (36)
Show source-provided references
1. A multi-source heterogeneous data fusion method and its application
2. Perspectives on future transportation research: Impact of intelligent transportation system technologies on next-generation transportation modeling10.1080/15472450.2012.710158 ↗
3. Data-driven intelligent transportation systems: A survey10.1109/TITS.2011.2158001 ↗
4. An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction
5. Parallel, Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications10.1109/TITS.2010.2060218 ↗
6. Dynamic near-term traffic flow prediction: System oriented approach based on past experiences10.1049/iet-its.2011.0123 ↗
7. Traffic flow prediction with big data: A deep learning approach
8. Artificial intelligence and big data10.1109/MIS.2013.39 ↗
9. Shi, L., Gangopadhyay, A., and Janeja, V.P. (2014). STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery. Knowl. Inf. Syst, 1–21.10.1007/s10115-014-0733-3 ↗
10. Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting10.1109/TITS.2019.2906365 ↗
11. Short-term prediction of traffic volume in urban arterials10.1061/(ASCE)0733-947X(1995)121:3(249) ↗
12. Combining Kohonen maps with ARIMA time series models to forecast traffic flow10.1016/S0968-090X(97)82903-8 ↗
13. Ding, A., Zhao, X., and Jiao, L. (2002, January 6). Traffic flow time series prediction based on statistics learning theory. Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, Singapore.
14. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results10.1061/(ASCE)0733-947X(2003)129:6(664) ↗
15. Deep learning10.1038/nature14539 ↗
16. DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction10.1109/TITS.2019.2932785 ↗
17. Nonparametric regression and short-term freeway traffic forecasting10.1061/(ASCE)0733-947X(1991)117:2(178) ↗
18. An aggregation approach to short-term traffic flow prediction10.1109/TITS.2008.2011693 ↗
19. A tutorial on support vector regression10.1023/B:STCO.0000035301.49549.88 ↗
20. Research on Short-Term Traffic Flow Forecasting Based on KNN and Discrete Event Simulation
21. Guo, S.N., Lin, Y.F., Feng, N., Song, C., and Wan, H.Y. (February, January 27). Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
22. Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7–13). Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.510 ↗
23. 3D convolutional neural networks for human action recognition10.1109/TPAMI.2012.59 ↗
24. Shi, X.J., Chen, Z.R., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7–12). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of theAdvances in Neural Information Processing Systems, Montreal, QC, Canada.
25. Zhang, J., Zheng, Y., and Qi, D. (2017, January 4–9). Deep spatiotemporal residual networks for citywide crowd flows prediction. Proceedings of the 31st AAAI Conference Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.10735 ↗
26. Missing traffic data: Comparison of imputation methods10.1049/iet-its.2013.0052 ↗
27. Tensor decompositions and applications10.1137/07070111X ↗
28. A tensor-based method for missing traffic data completion10.1016/j.trc.2012.12.007 ↗
29. Zambrano-Martinez, J.L., Calafate, C.T., Soler, D., Cano, J.C., and Manzoni, P. (2018). Modeling and characterization of traffic flows in urban environments. Sensors, 18.10.3390/s18072020 ↗
30. Short-term travel time prediction10.1016/S0968-090X(03)00026-3 ↗
31. Wu, J.Q., Wu, Q., Shen, J., and Cai, C. (2020). Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys. Sensors, 12.10.3390/s20123354 ↗
32. Short-Term Traffic Prediction Based on Dynamic Tensor Completion10.1109/TITS.2015.2513411 ↗
33. Prediction of a multi-mode coupling model based on traffic flow tensor data10.3233/JIFS-18804 ↗
34. Research on short-term traffic flow prediction based on the tensor decomposition algorithm10.3233/JIFS-201873 ↗
35. Yang, F.N., Liu, G.L., Huang, L.P., and Chin, C.S. (2020). Tensor Decomposition for Spatial-Temporal Traffic Flow Prediction with Sparse Data. Sensors, 21.10.3390/s20216046 ↗
36. Bao, L.C. (2018). Traffic Jam Prediction and Optimal Path Planning Based on Tensor Decomposition. [Master’s Thesis, Yunnan University].