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Disaggregate Logit Model of Public Transportation Share Ratio Prediction in Urban City

  • Dou Hui Li
  • Wang Guo Hua
Part of the Communications in Computer and Information Science book series (CCIS, volume 236)

Abstract

In order to analyze the distributing condition of urban passenger flow scientifically and correctly, the disaggregate Logit model is presented to predict the public transportation share ration in city, which is carried out by means of analysis of the outer and inner factors that affect the choice of modes of transportation and is based on the random utility theory. Firstly, the factors with major contribution to modes choice are selected according to the likelihood ratio statistic. Then the parameters of are estimated and the model is constructed. Finally, according to the proposed algorithm, the public transportation share ratio forecast test is carried out using the field survey data. The results of independent sample test indicate that the model has a finer precision and stability.

Keywords

Public transportation Share ratio prediction Disaggregate Logit model 

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References

  1. 1.
    Niu, X., Wang, W., Yin, Z.: Research on method of urban passenger traffic mode split forecast. Journal of highway and transportation research and development 21(3), 75–78 (2004)Google Scholar
  2. 2.
    Wang, Z., Liu, A., Zheng, P.: Generalized logit method for traffic modal splitting. Journal of Tongji University 27(3), 314–318 (1999)MathSciNetGoogle Scholar
  3. 3.
    Liu, Z., Deng, W., Guo, T.: Application of disaggregate model based on RP/SP survey to transportation planning. Journal of transportation engineering and information 6(3), 59–64 (2008)MathSciNetGoogle Scholar
  4. 4.
    Ghareib, A.H.: Evaluation of logit and probit models in mode-choice situation. Journal of transportation engineering 122(4), 282–290 (1996)Google Scholar
  5. 5.
    Liu, C.: Advanced traffic planning. China communications press, Beijing (2001)Google Scholar
  6. 6.
    Math department of Fudan University. Probability and mathematical statistics. People’s education press, Beijing (1979) Google Scholar
  7. 7.
    Wang, J., Guo, Z.: Logisitic regression models, method and application, vol. 9. Higher education press, Beijing (2001)Google Scholar
  8. 8.
    Yu, X., Ren, X.: Multivariable statistics analysis. China statistics press, Beijing (1999)Google Scholar
  9. 9.
    Hu, H., Teng, J., Gao, Y., et al.: Research on travel mode choice behavior under integrated multi-modal transit information service. China Journal of Highway and Transport 22(2), 87–92 (2009)Google Scholar
  10. 10.
    Dou, H., Wu, Z., Liu, H., et al.: Algorithm of Traffic State Probability Forecasting based on K Nearest Neighbor Nonparametric Regression. Journal of Highway and transportation research and development 27(8), 76–80 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dou Hui Li
    • 1
  • Wang Guo Hua
    • 2
  1. 1.Zhejiang Institute of CommunicationsHangzhouChina
  2. 2.Department of Traffic EngineeringZhejiang Provincial Institute of Communications Planning, Design and ResearchHangzhouChina

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