== Abstract ==

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Part 3: Computational Intelligence and Algorithms; International audience; Computational technologies under the domain of intelligent systems are expected to help the rapidly increasing traffic congestion problem in recent traffic management. Traffic management requires efficient and accurate forecasting models to assist real time traffic control systems. Researchers have proposed various computational approaches, especially in short-term traffic flow forecasting, in order to establish reliable traffic patterns models and generate timely prediction results. Forecasting models should have high accuracy and low computational time to be applied in intelligent traffic management. Therefore, this paper aims to evaluate recent computational modeling approaches utilized in short-term traffic flow forecasting. These approaches are evaluated by real-world data collected on the British freeway (M6) from 1st to 30th November in 2014. The results indicate that neural network model outperforms generalized additive model and autoregressive integrated moving average model on the accuracy of freeway traffic forecasting.

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Document type: Part of book or chapter of book

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== Full document ==

<pdf>Media:Yang_et_al_2015c-beopen3095-6858-document.pdf</pdf>

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== Original document ==

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

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* [https://hal.inria.fr/hal-01383959/file/371690_1_En_10_Chapter.pdf https://hal.inria.fr/hal-01383959/file/371690_1_En_10_Chapter.pdf] under the license https://creativecommons.org/licenses/by

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* [http://link.springer.com/content/pdf/10.1007/978-3-319-25261-2_10 http://link.springer.com/content/pdf/10.1007/978-3-319-25261-2_10],

: [http://dx.doi.org/10.1007/978-3-319-25261-2_10 http://dx.doi.org/10.1007/978-3-319-25261-2_10] under the license cc-by

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* [https://hal.inria.fr/hal-01383959 https://hal.inria.fr/hal-01383959],

: [https://hal.inria.fr/hal-01383959/document https://hal.inria.fr/hal-01383959/document],

: [https://hal.inria.fr/hal-01383959/file/371690_1_En_10_Chapter.pdf https://hal.inria.fr/hal-01383959/file/371690_1_En_10_Chapter.pdf] under the license http://www.springer.com/tdm

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* [https://link.springer.com/chapter/10.1007/978-3-319-25261-2_10 https://link.springer.com/chapter/10.1007/978-3-319-25261-2_10],

: [https://dblp.uni-trier.de/db/conf/ifip12/ai2015.html#YangDC15 https://dblp.uni-trier.de/db/conf/ifip12/ai2015.html#YangDC15],

: [https://hal.inria.fr/hal-01383959/document https://hal.inria.fr/hal-01383959/document],

: [https://hal.inria.fr/hal-01383959 https://hal.inria.fr/hal-01383959],

: [https://doi.org/10.1007/978-3-319-25261-2_10 https://doi.org/10.1007/978-3-319-25261-2_10],

: [https://rd.springer.com/chapter/10.1007/978-3-319-25261-2_10 https://rd.springer.com/chapter/10.1007/978-3-319-25261-2_10],

: [https://hal.archives-ouvertes.fr/hal-01383959v1 https://hal.archives-ouvertes.fr/hal-01383959v1],

: [https://academic.microsoft.com/#/detail/2187709084 https://academic.microsoft.com/#/detail/2187709084] under the license http://creativecommons.org/licenses/by/

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