Inferring left behind passengers in congested metro systems from automated data

•Developing performance metrics from the passenger’s point of view.•Estimating left behind probability using maximum likelihood or Bayesian inference.•Providing important input to passenger assignment models to improve accuracy.•Providing useful insights for route choice estimation. With subway syst...

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Bibliographic Details
Published inTransportation research. Part C, Emerging technologies Vol. 94; pp. 323 - 337
Main Authors Zhu, Yiwen, Koutsopoulos, Haris N., Wilson, Nigel H.M.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.09.2018
Subjects
Automated data
Bayesian estimation
Left behind
Maximum likelihood estimation
MCMC sampler
Passenger assignment
Automated data
Maximum likelihood estimation
Bayesian estimation
MCMC sampler
Passenger assignment
Left behind
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Summary:•Developing performance metrics from the passenger’s point of view.•Estimating left behind probability using maximum likelihood or Bayesian inference.•Providing important input to passenger assignment models to improve accuracy.•Providing useful insights for route choice estimation. With subway systems around the world experiencing increasing demand, measures such as passengers left behind are becoming increasingly important. This paper proposes a methodology for inferring the probability distribution of the number of times a passenger is left behind at stations in congested metro systems using automated data. Maximum likelihood estimation (MLE) and Bayesian inference methods are used to estimate the left behind probability mass function (LBPMF) for a given station and time period. The model is applied using actual and synthetic data. The results show that the model is able to estimate the probability of being left behind fairly accurately.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2017.10.002