Inferring left behind passengers in congested metro systems from automated data

Authors

Yiwen Zhu
Haris N. Koutsopoulos
Nigel H.M. Wilson

Document Type

Journal Article

Publication Date

2017

Subject Area

mode - subway/metro, technology - passenger information, ridership - demand

Keywords

Left behind, Automated data, Passenger assignment, Maximum likelihood estimation, Bayesian estimation, MCMC sampler

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.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

Recommended Citation

Zhu, Y., Koutsopoulos, H.N., & Wilson, N.H.M. (2017). Inferring left behind passengers in congested metro systems from automated data. Transportation Research Part C: Emerging Technologies, Available online 10 November 2017.In Press, Corrected Proof.