Communications - Scientific Letters of the University of Zilina 2010, 12(3):80-84 | DOI: 10.26552/com.C.2010.3.80-84

Neural Network Optimized by Genetic Algorithm of Models for Real-Time Forecast of Traffic Flow

Jun Hai-min1, Pei Yu-long2
1 School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Rd., Harbin 150090, China and Dalian Urban Planning & Design Institute, 186 Changchun Rd., Dalian 116011, China
2 School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Rd., Harbin 150090, China

Short-term traffic flow forecast plays an important role in transit scheduling. A high-order generalized neural network model is constructed to actualize dynamic forecast on-line and a hybrid genetic algorithm and identical dimension recurrence idea are performed to optimize the structure and shape of neural network dynamically so as to enhance its forecast accuracy. With data collected from Dazhi Str., Harbin as the system input, the experimental result indicates that the average relative error of forecast is 5.53% and the maximum is less than 21%, which proves that the proposed neural network model can satisfy the precision request, accelerate the convergence speed, improve the global generalization ability and possess the practicality in short-term traffic flow forecast.

Keywords: traffic flow forecast; neural network; genetic algorithm; relative error

Published: September 30, 2010  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Hai-min, J., & Yu-long, P. (2010). Neural Network Optimized by Genetic Algorithm of Models for Real-Time Forecast of Traffic Flow. Communications - Scientific Letters of the University of Zilina, 12(3), 80-84. doi: 10.26552/com.C.2010.3.80-84
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. EBERLEIN, X. J., WILSON, N. H. M., BARNHART, C., et al: Real-time Deadheading Problem in Transit Operations Control, Transp. Res. B Methodol., Vol. 32B, No. 2, pp. 77-100, 1998. Go to original source...
    2. SITE, P. D, FILIPPI, F.: Service Optimization for Bus Corridors with Short-turn Strategies and Variable Vehicle Size, Transp. Res. A Policy Pract., Vol. 32, No. 1, pp. 19-38, 1998. Go to original source...
    3. Kai, C., Mo, Z., Hamamatsu, Y.: Traffic Flow Forecasting Using the DESFM Model, IEEJ Trans. Electron. Inf. Syst., Vol. 126, No. 2, pp. 255-263, 2006 Go to original source...
    4. LEDOUX, C.: An Urban Traffic Flow Model Integrating Neural Networks, Transport. Res. C Emerging. Technol., Vol. 5, No. 5, pp. 287-300, 1997. Go to original source...
    5. WANG, Q., SHAO, H. H., ZHANG, Z. J.: Genetic Evolved Neural Network and Its Application in Formaldehyde Process Modeling and Optimization, J. Shanghai Jiaotong Univ. Sci., Vol. 30, No. 4, pp. 143-150, 1996.
    6. HU, R. M., LI, D. R., XU, Z. Q., et al: Generalized Information Storing Principle and Higher-order Generalized Neural Networks, Acta Electronica Sinica, Vol. 24, No. 7, pp. 59-65, 1996.
    7. XIANG, G., DONG, D.: Activation Functions and a Method to Improve the Training of Neural Networks by Using BP Algorithm, J. of Computer Research & Development, Vol. 34, No. 2, pp. 113-117, 1997.
    8. ZHANG, J., WALTER, G. G, MIAO, Y. B. et al: Wavelet Neural Network for Function Learning, IEEE Trans. Signal Process., Vol. 43, No. 6, pp. 1485-1497, 1995. Go to original source...
    9. FENG, J. N., DIAO, Z. J., YANG, X. B., et al: Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm, Syst. Eng. Electron., Vol. 27, No. 6, pp. 1112-1114, 2005.
    10. KIM, K. H., PARK, J. K., HWANG, K. J., et al: Implementation of Hybrid Short-term Forecasting System Using Artificial Neural Networks and Fuzzy Expert Systems, IEEE Trans. Power Syst., Vol. 10, No. 3, pp. 1534-1539, 1995. Go to original source...
    11. PEI, Y. L., WANG, Y. G.: Real-time Optimization Algorithm with Evolving Fuzzy Wavelet Neural Network for Passenger Flow Prediction in Dynamic Transit Scheduling, Dynamic of Continuous, Discrete, and Impulsive Systems, Series A Mathematical Analysis, Vol. 13, No. c, pp. 666-670, 2006.

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content