ABSTRACT
A new method for traffic flow forecasting based on multitask ensemble learning, which combines the advantages of multitask learning and ensemble learning, is proposed. Traditional traffic flow forecasting methods are a single task learning mode, which may neglect potential rich information embedded in some related tasks. In contrast to this, multitask learning can integrate information from related tasks for effective induction. Recent developments also witness the potential of ensemble learning for traffic flow forecasting. This paper devises a new method named MTLBag, a combination of multitask learning and a famous ensemble learning method bagging, for traffic flow forecasting.
Using a neural network predictor, this paper first empirically shows the superiority of multitask learning over single task learning for traffic flow forecasting. Experimental results also indicate that the performance of MTLBag is statistically significantly better than that of the multitask neural network predictor, and that MTLBag outperforms a state-of-the-art method Bayesian networks.
- L. Breiman. Bagging predictors. Mach. Learn., 24(2):123--140, August 1996. Google ScholarCross Ref
- R. Caruana. Multitask learning. Mach. Learn., 28(1):41--75, July 1997. Google ScholarDigital Library
- G. Davis and N. Nihan. Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng., 177(2):178--188, March/April 1991.Google ScholarCross Ref
- R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley and Sons, New York, 2001. Google ScholarDigital Library
- F. Jin and S. Sun. Neural network multitask learning for traffic flow forecasting. In Proc. Int. Joint Conf. Neural Networks, pages 1898--1902, 2008.Google Scholar
- J. Kittler, M. Hatef, R. Duin, and J. Matas. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226--239, March 1998. Google ScholarDigital Library
- I. Okutani and Y. Stephanedes. Dynamic prediction of traffic volume through kalman filter theory. Transp. Res., Part B: Methodol., 18 B(1):1--11, February 1984.Google Scholar
- S. Sun and C. Zhang. The selective random subspace predictor for traffic flow forecasting. IEEE Trans. Intell. Transp. Syst., 8(2):367--373, June 2007. Google ScholarDigital Library
- S. Sun, C. Zhang, and G. Yu. A bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst., 7(1):124--132, March 2006. Google ScholarDigital Library
- E. Yu and C. Chen. Traffic prediction using neural networks. In Proc. IEEE Global Telecommun. Conf., pages 991--995, 1993.Google ScholarCross Ref
- G. Yu, J. Hu, C. Zhang, L. Zhuang, and J. Song. Short-term traffic flow forecasting based on markov chain model. In Proc. IEEE Intell. Vehicles Symp., pages 208--212, 2003.Google Scholar
Index Terms
Traffic flow forecasting based on multitask ensemble learning
Recommendations
Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm
Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. However, the information of inter-urban traffic presents a challenging situation; the traffic flow ...
Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting
Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting ...
Incremental Learning for Multitask Pattern Recognition Problems
ICMLA '08: Proceedings of the 2008 Seventh International Conference on Machine Learning and ApplicationsThis paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are ...
Comments