A Novel Traffic Flow Forecasting Method Based on the Artificial Neural Networks and Intelligent Transportation Systems Data Mining

Abstract:

Useful information often hides in traffic management system. To mine useful data, prior knowledge has been used to train the artificial neural network (ANN) to identify the traffic conditions in the traffic information forecasting. Subjective information has hence been introduced into the ANN model. To solve this problem, a new ANN model is proposed based on the data mining technology in this work. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) to validate the performance of the proposed ANN model. The results show that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%.

• Periodical: Advanced Materials Research (Volume 842)
• Pages: 708-711
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.842.708
• Online since: November 2013
• Authors: Wei He*, Tao Lu, Cheng Qiang Yu
• Keywords: Artificial Neural Network (ANN), Data Mining (DM), Optimization, Traffic Forecasting

References

[1] Hauser, T. and W. Scherer, 2001. Data mining tools for real time traffic signal decision support and maintenance. In: Proc. of The IEEE International Conference on Systems, Man , and Cybernetics, Vol. 3 (2001), p.1471–1477.

DOI: 10.1109/icsmc.2001.973490

[2] Jia, L., L. Yang, Q. Kong and S. Lin, 2006. Study of artificial immune clustering algorithm and its applications to urban traffic control. International Journal of Information Technology, 12: 1–9.

[3] Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. In: Proc. of IEEE International Coference on Neural Networks, Piscataway, Vol. 1 (1995), p.1942–(1948).

[4] Krishna, B., 2012. Binary phase coded sequence generation using fractional order logistic equation. Circuits, Systems, and Signal Processing, 31(1): 401–411.

DOI: 10.1007/s00034-011-9295-8

[5] Li, Z., X. Yan, C. Yuan, Z. Peng and L. Li, 2011b. Virtual Prototype and Experimental Research on Gear Multi-fault Diagnosis Using Wavelet-Autoregressive Model and Principal Component Analysis Method. Mechanical Systems and Signal Processing, 25: 2589–2607.

DOI: 10.1016/j.ymssp.2011.02.017

[6] Li, Z., X. Yan, Z. Guo, P. Liu, C. Yuan and Z. Peng, 2012b. A new intelligent fusion method of multi-dimensional sensors and its application to tribo-system fault diagnosis of marine diesel engines. Tribology Letters, 47: 1–15.

DOI: 10.1007/s11249-012-9948-1

[7] Li, Z., X. Yan, C. Yuan and Z, Peng, 2012c. Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed. Journal of Mechanical Science and Technology, 26(8): 2413–2423.

DOI: 10.1007/s12206-012-0621-2

[8] Nejad, S., F. Seifi, H. Ahmadi and N. Seifi, 2009. Applying Data Mining in Prediction and Classification of Urban Traffic. In: Proc. of the 2009 WRI World Congress on Computer Science and Information Engineering, Vol. 3 (2009), p.674–678.

DOI: 10.1109/csie.2009.906

[9] Park, B., D. Lee and H. Yun, 2003. Enhancement of Time of Day Based Traffic Signal Control. In: Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, Vol. 4. (2003), p.3619–3624.

DOI: 10.1109/icsmc.2003.1244451

[10] Xu, P. and S. Lin, 2009. Internet traffic classification using C4. 5 decision tree. Journal of Software, 20(10): 2692–2704.

DOI: 10.3724/sp.j.1001.2009.03444