JACIII Vol.24 No.7 pp. 829-836
doi: 10.20965/jaciii.2020.p0829


FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

Di Yang, Ningjia Qiu, Peng Wang, and Huamin Yang

School of Computer Science and Technology, Changchun University of Science and Technology
No.7186 Weixing Road, Chaoyang District, Changchun, Jilin 130022, China

Corresponding author

October 4, 2020
October 27, 2020
December 20, 2020
traffic flow prediction, fused ridge, multi-task learning, noise data

Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and pairwise differences between neighboring coefficients, has been widely used to analyze highly correlated features with a natural order, as is the case with traffic flow. It denoises data by encouraging both sparsity of coefficients and their differences, and estimates the coefficients of highly correlated variables to be equal to each other. However, for traffic data, the same coefficients will lead to overexpression of features, and losing the trend of time series of traffic flow. In this work, we propose a Fused Ridge multi-task learning (FR-MTL) model for multi-road traffic flow prediction. It introduces Fused Ridge for traffic data denoising, imposes penalty on L2-norm of the coefficients and their differences. The penalty of L2-norm proportionally shrinks coefficients, and generates smooth coefficient vectors with non-sparsity. It has both capability of trend preservation and denoising. In addition, we jointly consider multi-task learning (MTL) for training shared spatiotemporal information among traffic roads. The experiments on real traffic data show the advantages of the proposed model over other four regularized baseline models, and on traffic data with Gaussian noise and missing data, the FR-MTL model demonstrates potential and promising capability with satisfying accuracy and effectiveness.

Cite this article as:
D. Yang, N. Qiu, P. Wang, and H. Yang, “FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.7, pp. 829-836, 2020.
Data files:
  1. [1] J. Tang, G. Zhang, Y. Wang, H. Wang, and F. Liu, “A Hybrid Approach to Integrate Fuzzy C-Means Based Imputation Method with Genetic Algorithm for Missing Traffic Volume Data Estimation,” Transportation Research Part C: Emerging Technologies, Vol.51, pp. 29-40, 2015.
  2. [2] A. Cheng, X. Jiang, Y. Li, C. Zhang, and H. Zhu, “Multiple Sources and Multiple Measures Based Traffic Flow Prediction Using the Chaos Theory and Support Vector Regression Method,” Physica A: Statistical Mechanics and Its Applications, Vol.466, pp. 422-434, 2017.
  3. [3] K. Kumar and V. K. Jain, “Autoregressive Integrated Moving Averages (ARIMA) Modelling of a Traffic Noise Time Series,” Applied Acoustics, Vol.58, No.3, pp. 283-294, 1999.
  4. [4] C. Qi and Z.-S. Hou, “Application of adaptive single-exponent smoothing for short-term traffic flow prediction,” Control Theory and Applications, Vol.29, No.4, pp. 465-469, 2012 (in Chinese).
  5. [5] S. R. Krishnan and C. S. Seelamantula, “On the Selection of Optimum Savitzky-Golay Filters,” IEEE Trans. on Signal Processing, Vol.61, No.2, pp. 380-391, 2013.
  6. [6] G. Chang, Y. Zhang, and D. Yao, “Missing Data Imputation for Traffic Flow Based on Improved Local Least Squares,” Tsinghua Science and Technology, Vol.17, No.3, pp. 304-309, 2012.
  7. [7] D. Boto-Giralda, F. J. Díaz-Pernas, D. González-Ortega, J. F. Díez-Higuera, M. Antón-Rodríguez, M. Martínez-Zarzuela, and I. Torre-Díez, “Wavelet-Based Denoising for Traffic Volume Time Series Forecasting with Self-Organizing Neural Networks,” Computer-Aided Civil and Infrastructure Engineering, Vol.25, No.7, pp. 530-545, 2010.
  8. [8] G. He, S. Ma, and Y. Li, “Study on the Short-term Forecasting for Traffic Flow Based on Wavelet Analysis,” Systems Engineering – Theory & Practice, Vol.22, No.9, pp.101-106, 2002 (in Chinese).
  9. [9] N. Garg, M. K. Sharma, K. S. Parmar, K. Soni, R. K. Singh, and S. Maji, “Comparison of ARIMA and ANN Approaches in Time-Series Predictions of Traffic Noise,” Noise Control Engineering J., Vol.64, No.4, pp. 522-531, 2016.
  10. [10] R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, “Sparsity and Smoothness via the Fused Lasso,” J. of The Royal Statistical Society Series B: Statistical Methodology, Vol.67, No.1, pp. 91-108, 2005.
  11. [11] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear Total Variation Based Noise Removal Algorithms,” Physica D: Nonlinear Phenomena, Vol.60, Nos.1-4, pp. 259-268, 1992.
  12. [12] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” J. of the Royal Statistical Society Series B: Methodological, Vol.58, No.1, pp. 267-288, 1996.
  13. [13] Transportation Research Board of the National Academies of Science, “Highway Capacity Manual,” Vols.1-4, 2010.
  14. [14] M. S. Ahmed and A. R. Cook, “Analysis of Freeway Traffic Time-Series Data by Using Box – Jenkins Techniques,” Transportation Research Record, No.722, 1979.
  15. [15] B. M. Williams and L. A. Hoel, “Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results,” J. of Transportation Engineering, Vol.129, No.6, pp. 664-672, 2003.
  16. [16] M.-C. Tan, S. C. Wong, J.-M. Xu, Z.-R. Guan, and P. Zhang, “An Aggregation Approach to Short-Term Traffic Flow Prediction,” IEEE Trans. on Intelligent Transportation Systems, Vol.10, No.1, pp. 60-69, 2009.
  17. [17] S. V. Kumar and L. Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” European Transport Research Review, Vol.7, No.3, Article No.21, 2015.
  18. [18] F. Moretti, S. Pizzuti, S. Panzieri, and M. Annunziato, “Urban Traffic Flow Forecasting Through Statistical and Neural Network Bagging Ensemble Hybrid Modeling,” Neurocomputing, Vol.167, pp. 3-7, 2015.
  19. [19] E. Castillo, J. M. Menéndez, and S. Sánchez-Cambronero, “Predicting Traffic Flow Using Bayesian Networks,” Transportation Research Part B: Methodological, Vol.42, No.5, pp. 482-509, 2008.
  20. [20] H. Zhou, S. Mabu, W. Wei, K. Shimada, and K. Hirasawa, “Traffic Flow Prediction with Genetic Network Programming (GNP),” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.6, pp. 713-725, 2009.
  21. [21] C. Li, Y. Lv, J. Yi, and G. Zhang, “Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1181-1191, 2016.
  22. [22] D. Chen, “Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network,” IEEE Trans. on Industrial Informatics, Vol.13, No.4, pp. 2000-2008, 2017.
  23. [23] X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang, and Y. Wang, “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors, Vol.17, No.4, Article No.818, 2017.
  24. [24] R. Fu, Z. Zhang, and L. Li, “Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction,” 2016 31st Youth Academic Annual Conf. of Chinese Association of Automation (YAC), Vol.2016, pp. 324-328, 2016.
  25. [25] B. Xin, Y. Kawahara, Y. Wang, and W. Gao, “Efficient Generalized Fused Lasso and Its Application to the Diagnosis of Alzheimer’s Disease,” Proc. of the 28th AAAI Conf. on Artificial Intelligence, pp. 2163-2169, 2014.
  26. [26] S. H. Lee, D. Yu, A. H. Bachman, J. Lim, and B. A. Ardekani, “Application of Fused Lasso Logistic Regression to the Study of Corpus Callosum Thickness in Early Alzheimer’s Disease,” J. of Neuroscience Methods, Vol.221, pp. 78-84, 2014.
  27. [27] A. Parekh and I. W. Selesnick, “Convex Fused Lasso Denoising with Non-Convex Regularization and Its Use for Pulse Detection,” 2015 IEEE Signal Processing in Medicine and Biology Symp. (SPMB), pp. 1-6, 2015.
  28. [28] D. Yu, S. J. Lee, W. J. Lee, S. C. Kim, J. Lim, and S. W. Kwon, “Classification of Spectral Data Using Fused Lasso Logistic Regression,” Chemometrics and Intelligent Laboratory Systems, Vol.142, pp. 70-77, 2015.
  29. [29] C. Park and S. B. Kim, “Virtual Metrology Modeling of Time-Dependent Spectroscopic Signals by a Fused Lasso Algorithm,” J. of Process Control, Vol.42, pp. 51-58, 2016.
  30. [30] S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. on Knowledge and Data Engineering, Vol.22, No.10, pp. 1345-1359, 2010.
  31. [31] California Department of Transportation, “Caltrans Performance Measurement System (PeMS),” 2019, [accessed January 16, 2019]

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