A Prediction Model Based on Time Series Data in Intelligent Transportation System

  • Jun Wu
  • Luo Zhong
  • Lingli Li
  • Aiyan Lu
Part of the Communications in Computer and Information Science book series (CCIS, volume 392)


Intelligent Transportation System has a new kind of complicated time series data which would be the traffic flow, average speed or some other traffic condition information at the same time period. All above data is useful and important for our traffic system which includes the traffic flow prediction, tendency analysis or cluster. With the development in time series analysis model and their applications, it is important to focus on how to find the useful and real-time traffic information from the Intelligent Transportation System. Using this method of building models for the Intelligent Transportation System is the way to solve the traffic prediction problem and make control of the massive traffic network.


Time Series Data Prediction Model Data Mining Intelligent Transportation System 


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  1. 1.
    Beresford, A.R., Bacon, J.: Intelligent Transportation System. IEEE Pervasive Computing 5(4), 63–67 (2006)CrossRefGoogle Scholar
  2. 2.
    Bunn, D.W., Karakatsani, N.: Forecasting electricity prices, London Business School Working Paper (2003)Google Scholar
  3. 3.
    Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multi-dimensional regression analysis of time-series data streams. In: VLDB 2002 Proceedings of the 28th International Conference on Very Large Data Bases, pp. 323–334 (2002)Google Scholar
  4. 4.
    Zadeh, L.A.: The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent systems. In: Proceedings of the 6th IEEE International Conference on Fuzzy Systems, Barcelona, Spain (1997)Google Scholar
  5. 5.
    Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining Data Streams. A Review.ACM SIGMOD Record Homepage Archive 34(2), 18–26 (2005)CrossRefGoogle Scholar
  6. 6.
    Nicolaisen, J.D., Richter Jr., C.W., Sheblé, G.B.: Price signal analysis for competitive electric generation companies. In: Proceedings of the International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, London, UK, pp. 66–71 (2000)Google Scholar
  7. 7.
    Kohzadi, N., Boyd, M.S., Kermanshahi, B.: A comparison of Artificial neural Network and Time Series Models for forecasting commodity prices. Neural Computing 10(2), 169–181Google Scholar
  8. 8.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and NN model. Neural Computing, 159–175 (2003)Google Scholar
  9. 9.
    Saini, L.M., Soni, M.K.: Artificial neuralnetwork based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods. IEEE Proc. -Gener. Transm. 149(5), 578–584 (2002)CrossRefGoogle Scholar
  10. 10.
    Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Network. 5(6), 989–993 (1994)Google Scholar
  11. 11.
    Fahrmair, M., Spanfelner, B.: Security and privacy rights management for mobile and ubiquitous computing. In: Workshop on UbiComp Privacy, Tokyo, Japan, September 11 (2005)Google Scholar
  12. 12.
    Huang, E., Antoniou, C., Wen, Y., Ben-Akiva, M.: Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models. In: Intelligent Transportation Systems, pp. 1–6. ITSC (2009)Google Scholar
  13. 13.
    Kang, Y., Lee, H., Chun, K., Song, J.: Classification of Privacy Enhancing Technologies on Life-cycle of Information. In: Proceeding of The International Conference on Emerging Security Information, Systems, and Technologies, pp. 66–70 (2007)Google Scholar
  14. 14.
    Breeden, J.L.: Modeling data with multiple time dimensions. Computational Statistics and Data Analysis 51(9), 4761–4785 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Tseng, F.-M.: Fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Systems 126, 367–376 (2002)zbMATHGoogle Scholar
  16. 16.
    Jang, J.S.: Predicting chaotic time series with fuzzy if-then rules. In: IEEE International Conference on Fuzzy Systems, San Francisco, USA, pp. 1079–1084 (1993)Google Scholar
  17. 17.
    Liao, Kao, W.-H., Fan, Y., Ming, C.: Data aggregation in wireless sensor networks using ant colony algorithm. Journal of Network and Computer Applications 31(4), 387–401 (2008)CrossRefGoogle Scholar
  18. 18.
    Cooper, J., James, A.: Challenges for database management in the internet of things.  Source. IETE Technical Review 26(5), 320–329 (2009)CrossRefGoogle Scholar
  19. 19.
    Ritchie, K.M.S.: Comparison of Traditional and Neural Classifiers for Pavement Crack Detection. ASCE 120(4), 552–569 (1994)Google Scholar
  20. 20.
    Breeden, J.L.: Modeling data with multiple time dimensions. Computational Statistics and Data Analysis 51(9), 4761–4785Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jun Wu
    • 1
    • 2
  • Luo Zhong
    • 2
  • Lingli Li
    • 2
  • Aiyan Lu
    • 2
  1. 1.School of Computer ScienceHubei University of TechnologyWuhanP.R. China
  2. 2.School of Computer Science and TechnologyWuhan University of TechnologyWuhanP.R. China

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