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

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.

KEYWORDS:

• Urban rail transit
• train operation
• train journey time
• probability distribution

Acknowledgements

We thank Professor Pu Wang and Professor Helai Huang at Central South University in the People’s Republic of China, and Professor Marta C. González at MIT, United States, for their interest in this study and useful discussions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Maosheng Li http://orcid.org/0000-0003-2799-4490

Additional information

Funding

This work was supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [grant number 20121707], the National Social Science Fund [grant number 14BJT017], the National natural Science Fund [grant number U1334207] and the Mathematics and Interdisciplinary Science Project of Central South University in 2014.