基于张量模型的短时交通流量预测
Short-Term Traffic Flow Prediction Based on Tensor Model
DOI: 10.12677/AAM.2023.1210443, PDF,   
作者: 周昱竹, 张仲荣*：兰州交通大学数理学院，甘肃 兰州；张其斌*：甘肃省计算中心，甘肃 兰州
关键词: 交通流量预测；张量分解；张量结构；Traffic Flow Forecast； Tensor Decomposition； Tensor Structure
摘要: 交通流量预测可以提供未来交通状况的预测结果，为交通规划提供有力支持。为实现高效的实时短期交通流预测，必须综合考虑时间、空间、天、周等多个关键维度。这种多维数据的处理形成了一种复杂的高维结构。然而，传统的线性和非线性模型所使用的向量和矩阵结构已经不再适用于处理这样的高维数据。因此，在本研究中，我们提出了一种高维数据结构，即张量模型。这个模型不仅可以有效地捕捉多维数据之间的复杂关系，还能够提供实时且准确的交通流预测。通过引入张量模型，我们能够更好地理解和利用多维数据，为交通管理和规划提供了新的工具和视角。
Abstract: Traffic flow forecast can provide the forecast results of future traffic conditions and provide strong support for traffic planning. In order to realize efficient real-time short-term traffic flow prediction, it is necessary to consider several key dimensions such as time, space, day and week. The pro-cessing of multi-dimensional data forms a complex high-dimensional structure. However, the vector and matrix structures used by traditional linear and nonlinear models are no longer suitable for processing such high-dimensional data. Therefore, in this study, we propose a high-dimen- sional data structure, the tensor model. This model can not only effectively capture the complex relation-ship between multi-dimensional data, but also provide real-time and accurate traffic flow prediction. By introducing tensor models, we are able to better understand and utilize multidimensional data, providing new tools and perspectives for traffic management and planning.
文章引用：周昱竹, 张仲荣, 张其斌. 基于张量模型的短时交通流量预测[J]. 应用数学进展, 2023, 12(10): 4519-4528. https://doi.org/10.12677/AAM.2023.1210443

参考文献

[1] George, S. and Sanntra, A.K. (2020) Traffic Prediction Using Multifaceted Techniques: A Survey. Wireless Personal Communications, 115, 1047-1106. [Google Scholar] [CrossRef] 
[2] Abdulaziz, A., Humera, Y., Hafsa, M., et al. (2018) Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS). Sensors, 18, Article 1983. [Google Scholar] [CrossRef] [PubMed]
[3] Ferreira, M. and D’Orey, P.M. (2012) On the Impact of Virtual Traffic Lights on Carbon Emissions Mitigation. IEEE Transactions on Intelligent Transporta-tion Systems, 13, 284-295. [Google Scholar] [CrossRef] 
[4] Nellore, K. and Hancke, G. (2016) A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors, 16, Article 157. [Google Scholar] [CrossRef] [PubMed]
[5] Chisci, L., Mavino, A., Perferi, G., et al. (2010) Real-Time Epileptic Sei-zure Prediction Using AR Models and Support Vector Machines. IEEE Transactions on Biomedical Engineering, 57, 1124-1132. [Google Scholar] [CrossRef] 
[6] Ehara, H. and Yoshida, K. (2008) Interpolative Frame-Erasure Concealment of Moving-Average Prediction Coding Parameters in ITU-T Recommendation G.718. Electronics Letters, 44, 1381-1382. [Google Scholar] [CrossRef] 
[7] Kanoulas, E., Yang, D., Tian, X. and Zhang, D.H. (2006) Finding Fastest Paths on a Road Network with Speed Patterns. 22nd International Conference on Data Engineering (ICDE’06), Atlanta, 3-7 April 2006, 10. [Google Scholar] [CrossRef] 
[8] Wang, Y., Li, L. and Xu, X. (2017) A Piecewise Hybrid of ARIMA and SVMs for Short-Term Traffic Flow Prediction. In: Liu, D., Xie, S., Li, Y., Zhao, D. and El-Alfy, E.S., Eds., ICONIP 2017: Neural Information Processing, Springer, Cham, 493-502. [Google Scholar] [CrossRef] 
[9] Yao, R.H., Zhang, W.S. and Zha, L.H. (2020) Hybrid Meth-ods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network. Journal of Transportation Engineering Part A Systems, 146, Article ID: 04020086 [Google Scholar] [CrossRef] 
[10] Lu, S.Q., Zhang, Q.Y., Chen, G.S., et al. (2021) A Combined Method for Short-Term Traffic Flow Prediction Based on Recurrent Neural Network. Alexandria Engineering Journal, 60, 87-94. [Google Scholar] [CrossRef] 
[11] Williams, B.M. and Hoel, L.A. (2003) Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering, 129, 664-672. [Google Scholar] [CrossRef] 
[12] Voort, M., Dougherty, M. and Watson, S. (1996) Combining Kohonen Maps with ARIMA Time Series Models to Forcast Traffic Flow. Transportation Research Part C Emerging Technologies, 4, 307-318. [Google Scholar] [CrossRef] 
[13] Williams, B.M. (2001) Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMA Modeling. Transportation Research Record, 1776, 194-200. [Google Scholar] [CrossRef] 
[14] Davis, G.A. and Nihan, N.L. (1991) Nonparametric Regression and Short-Term Freeway Traffic Forecasting. Journal of Transportation Engineering, 117, 178-188. [Google Scholar] [CrossRef] 
[15] Xia, D., Wang, B., Li, H., Li, Y.T. and Zhang, Z.L. (2016) A Distributed Spatial-Temporal Weighted Model on Mapreduce for Short-Term Traffic Flow Forecasting. Neurocomputing, 179, 246-263. [Google Scholar] [CrossRef] 
[16] Chang, H., Lee, Y., Yoon, B. and Baek, S. (2012) Dynamic Near-Term Traffic Flow Prediction: System-Oriented Approach Based on Passed Experiences. IET Intelligent Transport Systems, 6, 292-305. [Google Scholar] [CrossRef] 
[17] Sun, B., Cheng, W., Goswami, P. and Bai, G.H. (2018) Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours. IET Intelligent Transport Systems, 12, 41-48. [Google Scholar] [CrossRef] 
[18] Luo, X.L., Li, D.Y. and Zhang, S.R. (2019) Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation, 2019, Article ID: 4145353. [Google Scholar] [CrossRef] 
[19] Xu, D.G., Wang, Y.D., Peng, P., et al. (2020) Real-Time Road Traffic State Prediction Based on Kernel-KNN. Transportmetrica A: Transport Science, 16, 104-118. [Google Scholar] [CrossRef] 
[20] Ge, W.L., Cao, Y., Ding, Z.M. and Guo, L.M. (2019) Fore-casting Model of Traffic Flow Prediction Model Based on Multi-Resolution SVR. Proceedings of the 2019 3rd Interna-tional Conference on Innovation in Artificial Intelligence, Suzhou, 15-18 March 2018, 1-5. [Google Scholar] [CrossRef] 
[21] Li, C. and Xu, P. (2021) Application on Traffic Flow Prediction of Machine Learning in Intelligent Transportation. Neural Computing and Applications, 33, 613-624. [Google Scholar] [CrossRef] 
[22] Angayarkanni, S.A., Sivakumar, R. and Rao, Y. (2021) Hybrid Grey Wolf: Bald Eagle Search Optimized Support Vector Regression for Traffic Flow Forecasting. Journal of Ambient Intelligence and Humanized Computing, 12, 1293-1304. [Google Scholar] [CrossRef] 
[23] Miyazaki, Y. (2011) Extending the Linear Model with R: Gen-eralized Linear, Mixed Effects and Nonparametric Regression Models. Applied Psychological Measurement, 35, 330-333. [Google Scholar] [CrossRef] 

为你推荐