Development of an Effective Travel Time Prediction Method Using Modified Moving Average Approach

  • Nihad Karim Chowdhury
  • Rudra Pratap Deb Nath
  • Hyunjo Lee
  • Jaewoo Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5711)

Abstract

Prediction of travel time on road network has emerged as a crucial research issue in intelligent transportation system (ITS). Travel time prediction provides information that may allow travelers to change their routes as well as departure time. To provide accurate travel time for travelers is the key challenge in this research area. In this paper, we formulate two new methods which are based on moving average can deal with this kind of challenge. In conventional moving average approach, data may lose at the beginning and end of a series. It may sometimes generate cycles or other movements that are not present in the original data. Our proposed modified method can strongly tackle those kinds of uneven presence of extreme values. We compare the proposed methods with the existing prediction methods like Switching method [10] and NBC method [11]. It is also revealed that proposed methods can reduce error significantly in compared with other existing methods.

Keywords

Intelligent transportation system travel time prediction moving average NBC method Switching method 

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References

  1. 1.
    Chen, M., Chien, S.: Dynamic freeway travel time prediction using probe vehicle data: Link-based vs. Path-based. J. of Transportation Research Record, TRB Paper No. 01-2887, Washington, DC, pp. 157-161 (2001)Google Scholar
  2. 2.
    Chun-Hsin, W., Chia-Chen, W., Da-Chun, S., Ming-Hua, C., Jan-Ming, H.: Travel Time Prediction with Support Vector Regression. In: IEEE Intelligent Transportation Systems Conference, vol. 2, pp. 1438–1442 (2003)Google Scholar
  3. 3.
    Kwon, J., Petty, K.: A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. In: Transportation Research Board 84th Annual Meeting, Washington, DC, pp. 147–153 (2005)Google Scholar
  4. 4.
    Park, D., Rilett, L.: Forecasting multiple-period freeway link travel times using modular neural networks. J. of Transportation Research Record 1617, 163–170 (1998)CrossRefGoogle Scholar
  5. 5.
    Park, D., Rilett, L.: Spectral basis neural networks for real-time travel time forecasting. J. of Transport Engineering 125(6), 515–523 (1999)CrossRefGoogle Scholar
  6. 6.
    Kwon, J., Coifman, B., Bickel, P.J.: Day-to-day travel time trends and travel time prediction from loop detector data. J. of Transportation Research Record, No. 1717, TRB, National Research Council, Washington, DC, pp. 120–129 (2000)Google Scholar
  7. 7.
    Zhang, X., Rice, J.: Short-Term Travel Time Prediction. Transportation Research Part C 11, 187–210 (2003)CrossRefGoogle Scholar
  8. 8.
    Van der Voort, M., Dougherty, M., Watson, S.: Combining KOHONEN maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C 4, 307–318 (1996)CrossRefGoogle Scholar
  9. 9.
    Rice, J., Van Zwet, E.: A simple and effective method for predicting travel times on freeways. IEEE Trans. Intelligent Transport Systems 5(3), 200–207 (2004)CrossRefGoogle Scholar
  10. 10.
    Schmitt Erick, J., Jula, H.: On the Limitations of Linear Models in Predicting Travel Times. In: IEEE Intelligent Transportation Systems Conference, pp. 830–835 (2007)Google Scholar
  11. 11.
    Lee, H., Chowdhury, N.K., Chang, J.: A New Travel Time Prediction Method for Intelligent Transportation Systems. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part I. LNCS (LNAI), vol. 5177, pp. 473–483. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Han, J., Kamber, M.: Data Mining: Concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2006)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nihad Karim Chowdhury
    • 1
  • Rudra Pratap Deb Nath
    • 1
  • Hyunjo Lee
    • 2
  • Jaewoo Chang
    • 2
  1. 1.Department of Computer Science & EngineeringUniversity of ChittagongBangladesh
  2. 2.Department of Computer EngineeringChonbuk National UniversitySouth Korea

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