== 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:Draft_Content_923140912-beopen44-8946-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 cc-by

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