Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution
Joint Authors
Kidando, Emmanuel
Moses, Ren
Ozguven, Eren E.
Sando, Thobias
Source
Journal of Advanced Transportation
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time.
Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions.
In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution.
In particular, a nonparametric Dirichlet Process Mixture Model (DPMM) with stick-breaking process representation was used.
The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation.
To reduce computational complexity, the modeling process was limited to a maximum of six components.
Then, the Markov Chain Monte Carlo (MCMC) sampling technique was employed to estimate the parameters’ posterior distribution.
Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time.
The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components.
The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.
American Psychological Association (APA)
Kidando, Emmanuel& Moses, Ren& Ozguven, Eren E.& Sando, Thobias. 2017. Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1170740
Modern Language Association (MLA)
Kidando, Emmanuel…[et al.]. Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution. Journal of Advanced Transportation No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1170740
American Medical Association (AMA)
Kidando, Emmanuel& Moses, Ren& Ozguven, Eren E.& Sando, Thobias. Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1170740
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170740