Scipediacontent: Scipediacontent moved page Draft Content 834064841 to 238,295qt

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Scipediacontent moved page Draft Content 834064841 to 238,295qt

Scipediacontent: Created page with " == Abstract == To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connecti..."

2021-03-23T04:42:31Z

Created page with " == Abstract == To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connecti..."

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== Abstract ==

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_834064841-beopen1462-3505-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/s18103459 http://dx.doi.org/10.3390/s18103459] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.3390/s18103459 http://dx.doi.org/10.3390/s18103459] under the license http://creativecommons.org/licenses/by/3.0/

* [http://europepmc.org/articles/PMC6210894 http://europepmc.org/articles/PMC6210894] under the license https://creativecommons.org/licenses/by/4.0

* [https://www.mdpi.com/1424-8220/18/10/3459/pdf https://www.mdpi.com/1424-8220/18/10/3459/pdf]

* [http://www.mdpi.com/1424-8220/18/10/3459 http://www.mdpi.com/1424-8220/18/10/3459],
: [https://doaj.org/toc/1424-8220 https://doaj.org/toc/1424-8220] under the license cc-by

* [http://www.mdpi.com/1424-8220/18/10/3459/pdf http://www.mdpi.com/1424-8220/18/10/3459/pdf],
: [http://dx.doi.org/10.3390/s18103459 http://dx.doi.org/10.3390/s18103459]

* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210894 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210894],
: [https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#GoudarziKASD18 https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#GoudarziKASD18],
: [http://repository.essex.ac.uk/23358 http://repository.essex.ac.uk/23358],
: [http://europepmc.org/articles/PMC6210894 http://europepmc.org/articles/PMC6210894],
: [http://eprints.utm.my/id/eprint/79667 http://eprints.utm.my/id/eprint/79667],
: [https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902247805787397 https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902247805787397],
: [https://doi.org/10.3390/s18103459 https://doi.org/10.3390/s18103459],
: [https://academic.microsoft.com/#/detail/2896326353 https://academic.microsoft.com/#/detail/2896326353] under the license https://creativecommons.org/licenses/by/4.0/

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Scipediacontent: Scipediacontent moved page Draft Content 834064841 to 238,295qt

Scipediacontent: Created page with " == Abstract == To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connecti..."