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Distribution analysis of train interval journey time employing the censored model with shifting character

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  • Maosheng Li
  • Zhengqiu Liu
  • Yonghong Zhang
  • Weijun Liu
  • Feng Shi

Abstract

The theoretical framework of limited dependent variable models is extended to accommodate a shifting character and thus fit the distribution of train journey time on sections of urban rail network. Data of actual train arrival and departure time at each station are used to calculate the journey time of each railway interval of multi-class trains. The log-normal distribution and normal distribution among a group of theoretical distributions are the most and second most suitable latent distributions of the train interval journey time in the censored models with shifting character. This modified distribution is described by four parameters, namely, the expectation and variance of the latent distribution and the upper and lower bound of the migration interval. The square root of the least square measurement (SRLSM) is taken as a measure, and a traversal search is adopted to determine the above four parameters according to the SRLSM. The average of the SRLSM of the theoretical train interval journey time distribution obtained by using the proposed method on all railway sections is 0.0905. The theoretical framework is the basis of storing hidden rules in data instead of past data of train travel time and optimizing the existing management of rail transit operation.

Suggested Citation

  • Maosheng Li & Zhengqiu Liu & Yonghong Zhang & Weijun Liu & Feng Shi, 2017. "Distribution analysis of train interval journey time employing the censored model with shifting character," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 715-733, March.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:715-733
    DOI: 10.1080/02664763.2016.1182134
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    1. Yi Zhao & Ronghui Liu & Xi Zhang & Anthony Whiteing, 2018. "A chance-constrained stochastic approach to intermodal container routing problems," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.

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