Scipediacontent: Scipediacontent moved page Draft Content 866695589 to Qin Yun 2018a

2021-02-12T13:19:57Z

Scipediacontent moved page Draft Content 866695589 to Qin Yun 2018a

Scipediacontent: Created page with " == Abstract == Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetrati..."

2021-02-12T13:19:54Z

Created page with " == Abstract == Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetrati..."

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== Abstract ==

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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_866695589-beopen318-6145-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://downloads.hindawi.com/journals/jat/2018/5148085.pdf http://downloads.hindawi.com/journals/jat/2018/5148085.pdf] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.1155/2018/5148085 http://dx.doi.org/10.1155/2018/5148085] under the license cc-by

* [http://downloads.hindawi.com/journals/jat/2018/5148085.pdf http://downloads.hindawi.com/journals/jat/2018/5148085.pdf],
: [http://downloads.hindawi.com/journals/jat/2018/5148085.xml http://downloads.hindawi.com/journals/jat/2018/5148085.xml],
: [http://dx.doi.org/10.1155/2018/5148085 http://dx.doi.org/10.1155/2018/5148085] under the license http://creativecommons.org/licenses/by/4.0

* [http://dx.doi.org/10.1155/2018/5148085 http://dx.doi.org/10.1155/2018/5148085],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2018/5148085 https://www.hindawi.com/journals/jat/2018/5148085],
: [http://downloads.hindawi.com/journals/jat/2018/5148085.pdf http://downloads.hindawi.com/journals/jat/2018/5148085.pdf],
: [https://academic.microsoft.com/#/detail/2883345462 https://academic.microsoft.com/#/detail/2883345462]

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Scipediacontent: Scipediacontent moved page Draft Content 866695589 to Qin Yun 2018a

Scipediacontent: Created page with " == Abstract == Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetrati..."