Open AccessStatistical inference on transit route‐level origin–destination flows using automatic passenger counter data
Author(s) -
Ji Yuxiong,
You Qiyuan,
Jiang Shengchuan,
Zhang Hongjun Michael
Publication year - 2015
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.1301
Subject(s) - markov chain monte carlo , computer science , enumeration , statistical inference , markov chain , inference , flow (mathematics) , algorithm , data mining , mathematical optimization , statistics , mathematics , bayesian probability , artificial intelligence , machine learning , geometry , combinatorics
Summary We consider inferring transit route‐level origin–destination (OD) flows using large amounts of automatic passenger counter (APC) boarding and alighting data based on a statistical formulation. One critical problem is that we need to enumerate the OD flow matrices that are consistent with the APC data for each bus trip to evaluate the model likelihood function. The OD enumeration problem has not been addressed satisfactorily in the literature. Thus, we propose a novel sampler to avoid the need to enumerate OD flow matrices by generating them recursively from the first alighting stop to the last stop of the bus route of interest. A Markov chain Monte Carlo (MCMC) method that incorporates the proposed sampler is developed to simulate the posterior distributions of the OD flows. Numerical investigations on an operational bus route under a realistic OD structure demonstrate the superiority of the proposed MCMC method over an existing MCMC method and a state‐of‐the‐practice method. Copyright © 2015 John Wiley & Sons, Ltd.