An Innovative Approach for the Short-term Traffic Flow Prediction

Abstract

Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people’s daily life. In the last twenty years, many traffic flow prediction approaches have been proposed. However, some of these approaches use the regression based mechanisms, which cannot achieve accurate short-term traffic flow predication. While, other approaches use the neural network based mechanisms, which cannot work well with limited amount of training data. To this end, a light weight tensor-based traffic flow prediction approach is proposed, which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time. In the proposed approach, first, a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals. Then, a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor. Finally, the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction. The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.

Keywords

Short-term traffic flow prediction / tensor / CP decomposition / limited amount of data

References

[1] Acar E, Rasmussen MA, Savorani F, Næs T, Bro R. Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemometrics & Intelligent Laboratory Systems, 2013, 129(22): 53-63.

[2] Booth DE. Multi-Way Analysis: Applications in the Chemical Sciences, 2004, USA: Wiley

[3] California Goverment (2007). California highway patrol. http://wvww.chp.ca.gov/.

[4] Castro-Neto M, Jeong YS, Jeong MK, Han LD. Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications an International Journal, 2009, 36: 6164-6173. 3-part-P2

[5] Chen C, Hu J, Meng Q, Zhang Y (2011). Short-time traffic flow prediction with arima-garch model. IEEE Intelligent Vehicles Symposium.

[6] Chen J, Tian Y, Ying S. Second International Conference on Computational Intelligence and Natural Computing, 2008

[7] Chen J, Tian Y, Ying S. Second International Conference on Computational Intelligence and Natural Computing, 2010

[8] Chen Q, Song Y, Zhao J. Neural Computing and Applications, 2020

[9] Dong H, Jia L, Sun X, Li C, Qin Y. International Conference on Networked Computing and Advanced Information Management, 2009

[10] Duan P, Mao G, Zhang C, Wang S. IEEE 19th International Conference on Intelligent Transportation Systems, 2016

[11] Fu R, Zhang Z, Li L. 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2016

[12] Gavirangaswamy VB, Gupta G, Gupta A, Agrawal R. Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems, 2013

[13] Han C, Song S, Wang CH. Acta Simulata Systematica Sinica, 2004

[14] Kolda TG, Bader BW. Tensor decompositions and applications. SIAM Review, 2009, 51(3): 455-500.

[15] Li Z, Li Y, Li L. A comparison of detrending models and multi-regime models for traffic flow prediction. Intelligent Transportation Systems Magazine IEEE, 2014, 6(4): 34-44.

[16] Li Y, Li Z, Li L. Missing traffic data: Comparison of imputation methods. IET Intelligent Transport Systems, 2014, 8(1): 51-57.

[17] Morup M, Dunlavy DM, Acar E, Kolda T ø. Siam International Conference on Data Mining, 2010

[18] Smith BL, Williams BM, Oswald RK. Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C, 2002, 10(4): 303-321.

[19] Tan H, Feng G, Feng J, Wang W, Zhang YJ, Li F. A tensor-based method for missing traffic data completion. Transportation Research Part C Emerging Technologies, 2013, 28: 15-27.

[20] Tan H, Wu Y, Feng G, Wang W, Ran B. A new traffic prediction method based on dynamic tensor completion. Procedia - Social and Behavioral Sciences, 2013, 96: 2431-2442.

[21] Tan H, Wu Y, Shen B, Jin PJ, Ran B. Short-term traffic prediction based on dynamic tensor completion. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8): 1-11.

[22] Tomasi G, Bro R. A comparison of algorithms for fitting the PARAFAC model. Computational Statistics & Data Analysis, 2006, 50(7): 1700-1734.

[23] Vazifehdan M, Moattar MH, Jalali M. A hybrid bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction. Journal of King Saud University — Computer and Information Sciences, 2018, 31(2): 175-84.

[24] Vlahogianni EI, Karlaftis MG, Golias JC. Spatio-temporal short-term urban traffic volume forecasting using genetically optimized modular networks. Computer-Aided Civil and Infrastructure Engineering, 2007, 22(5): 317-25.

[25] Wang Y, Li L, Xu X. International Conference on Neural Information Processing, 2017

[26] Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H. A distributed wnd-lstm model on mapreduce for short-term traffic flow prediction. Neural Computing and Applications, 2020, 33(7): 2393-410.

[27] Yi H, Jung H, Bae S. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), 2017

[28] Yin H, Wong S, Xu J, Wong CK. Urban traffic flow prediction using a fuzzy-neural approach. Transportation Research Part C, 2002, 10(2): 85-98.

[29] Zeng D, Xu J, Gu J, Liu L, Xu G. Workshop on Power Electronics and Intelligent Transportation System, 2008

[30] Zhao Q, Zhang L, Cichocki A. Bayesian cp factorization of incomplete tensors with automatic rank determination. IEEE Trans Pattern Anal Mach Intell, 2015, 37(9): 1751-1763.

[31] Zheng L, Yang J, Chen L, Sun D, Liu W. Dynamic spatial-temporal feature optimization with ERI big data for short-term traffic flow prediction. Neurocomputing, 2020, 412: 339-350.

[32] Zhu X, Li F. Traffic flow prediction based on artificial life and RBF neural network. Energy Procedia, 2014, 10(3): 1250-1254.