Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8947486 | Transportation Research Part C: Emerging Technologies | 2018 | 15 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Yiwen Zhu, Haris N. Koutsopoulos, Nigel H.M. Wilson,