International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Application Of Viterbi Algorithm For Efficient Transportation Forecasting

[Full Text]



Manas Kumar Yogi, Koondrapu Koushik Sri Sai, Afreen Jaha



Transportation forecasting, Viterbi algorithm, Hidden Markov Model, Prediction.



This paper discusses a novel application of probabilistic models which can uncover a hidden sequence of states thereby helping us predict the transportation needs during time where people will travel in huge numbers. We advocate the application of Viterbi algorithm for serving our purpose. The Viterbi algorithm has been already applied in various domains with remarkable efficiency forcing us to think about its role in supporting development of robust prediction models for railway transport. Our paper enlightens the strength of Viterbi algorithm and how its efficiency is comparable to other prediction models which considers the standard factors only limiting their conclusive prediction power. The experimental results prove that our proposed strategy improves prediction accuracy significantly than other forecasting models.



[1]. 1.J. J. Louviere, D. H. Henly, G. Woodworth, R. J. Meyer, I. P. Levin, J. W. Stones, D. Curry, and D. A. Anderson. Laboratory-Simulation Versus Revealed-Preference Methods for Estimating Travel Demand Models. In Transportation Research Record 794, TRB, National Research Council, Washington, D.C., 1981, pp. 42-51.
[2]. J. Bates. Stated Preference Technique for the Analysis of Transportation Behavior. Proc., 3rd WCTR, Hamburg, West Germany, 1983, pp. 252-265.
[3]. D. A. Hensher, P. 0. Barnard, and T. P. Thruong. The Role of Stated Preference Methods in Studies of Travel Choice. Journal of Transport Economics and Policy, Vol. 22, No. 1, 1988, pp. 45-58.
[4]. P. E. Green and V. Srinivasan. Conjoint Analysis in Consumer Research: Issues and Outlook. Journal of Consumer Research, Vol. 5, 1978, pp. 103-123.
[5]. P. Cattin and D. R. Wittink. Commercial Use of Conjoint Analysis: A Survey. Journal of Marketing, Vol. 46, 1982, pp. 44-53.
[6]. M. Ben-Akiva, T. Morikawa, and F. Shiroishi. Analysis of the Reliability of Stated Preference Data in Estimating Mode Choice Models. Selected Proc., 5th WCTR, Vol. 4, Yokohama, Japan, 1989, pp. 263-277.
[7]. M. Ben-Akiva and T. Morikawa. Estimation of Switching Models from Revealed Preferences and Stated Intentions. Transportation Research A, Vol. 24A, No. 6, 1990, pp. 485-495.
[8]. M. Ben-Akiva and T. Morikawa. Estimation of Travel Demand Models from Multiple Data Sources. Proc., 11th International Symposium on Transportation and Traffic Theory (M. Koshi, ed.), Elsevier, 1990, pp. 461-476.
[9]. T. Amemiya. Advanced Econometrics. Harvard University Press, Cambridge, Mass., 1985.
[10]. C. Manski and S. Lerman. The Estimation of Choice Probabilities from Choice-Based Samples. Econometrica, Vol. 45, 1977.