Scipediacontent: Scipediacontent moved page Draft Content 841335635 to Hashemi et al 2018a

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Scipediacontent moved page Draft Content 841335635 to Hashemi et al 2018a

Scipediacontent: Created page with " == Abstract == Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a m..."

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Created page with " == Abstract == Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a m..."

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== Abstract ==

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior. On the other hand, an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks. This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective optimizer for short-term prediction. NSGA-II is used to optimize the number of neurons in the first and second layers of the NN, learning ratio and slope of the activation function. This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way. Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway. Results are analyzed based on the performance measures, showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment. The achieved prediction accuracy is calculated with multiple measures such as the root mean square error (RMSE), and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction, respectively.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_841335635-beopen232-2002-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.1007/s40534-018-0179-5 http://dx.doi.org/10.1007/s40534-018-0179-5] under the license https://creativecommons.org/licenses/by

* [https://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf https://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf] under the license https://creativecommons.org/licenses/by/4.0

* [http://link.springer.com/article/10.1007/s40534-018-0179-5 http://link.springer.com/article/10.1007/s40534-018-0179-5],
: [https://doaj.org/toc/2095-087X https://doaj.org/toc/2095-087X],
: [https://doaj.org/toc/2196-0577 https://doaj.org/toc/2196-0577] under the license cc-by

* [http://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf http://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf],
: [http://link.springer.com/article/10.1007/s40534-018-0179-5/fulltext.html http://link.springer.com/article/10.1007/s40534-018-0179-5/fulltext.html],
: [http://dx.doi.org/10.1007/s40534-018-0179-5 http://dx.doi.org/10.1007/s40534-018-0179-5]

* [https://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf https://link.springer.com/content/pdf/10.1007/s40534-018-0179-5.pdf],
: [https://link.springer.com/article/10.1007/s40534-018-0179-5 https://link.springer.com/article/10.1007/s40534-018-0179-5],
: [http://www.cqvip.com/QK/85396A/201904/7100522633.html http://www.cqvip.com/QK/85396A/201904/7100522633.html],
: [https://paperity.org/p/181488388/traffic-prediction-using-a-self-adjusted-evolutionary-neural-network https://paperity.org/p/181488388/traffic-prediction-using-a-self-adjusted-evolutionary-neural-network],
: [https://sciforum.net/paper/view/52edc742c7fa9680f2b72f517a96242b https://sciforum.net/paper/view/52edc742c7fa9680f2b72f517a96242b],
: [https://academic.microsoft.com/#/detail/2906154449 https://academic.microsoft.com/#/detail/2906154449] under the license https://creativecommons.org/licenses/by/4.0

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Scipediacontent: Scipediacontent moved page Draft Content 841335635 to Hashemi et al 2018a

Scipediacontent: Created page with " == Abstract == Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a m..."