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Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches
Joint Authors
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-24
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states.
In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD.
First, link travel times are classified by different traffic states according to the levels of vehicle delays.
Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations.
Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities.
Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function.
Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles.
The proposed approach is evaluated using real-world FCD.
The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions.
In addition, the approach with different percentage of floating cars is tested.
The results are encouraging, even when multimodal distributions and very few or no observations exist.
American Psychological Association (APA)
Qin, Wenwen& Yun, Meiping. 2018. Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1181364
Modern Language Association (MLA)
Qin, Wenwen& Yun, Meiping. Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches. Journal of Advanced Transportation No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1181364
American Medical Association (AMA)
Qin, Wenwen& Yun, Meiping. Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1181364
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181364