A Comparative Study Of Road Traffic Forecasting Models
dc.contributor.author | Benabdallah Benarmas, Redouane | |
dc.contributor.author | Beghdad, Kadda | |
dc.date.accessioned | 2022-04-12T10:24:59Z | |
dc.date.available | 2022-04-12T10:24:59Z | |
dc.date.issued | 2022-01-24 | |
dc.description | Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued | en_US |
dc.description.abstract | In the context of Intelligent Transport Systems (ITS), the behaviour of road traffic has been the subject of many theoretical and experimental researches. In the last decade, road prediction is placed as the first line of research in this field. The problem has been solved with a variety of models to assist the traffic control, this includes, improving the efficiency of transport, guidance in the road, and smart coordination signals. This paper tries to synthesize the carried out, on three main approaches, namely based on statistical methods, time series and deep learning.A comparatives synthesis in terms quantitative and qualitative index of is provided in order to evaluate the performance and potential of the three forecasting approaches. | en_US |
dc.identifier.citation | Benabdallah Benarmas, Redouane, Beghdad, Kadda. A Comparative Study Of Road Traffic Forecasting Models. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here] | en_US |
dc.identifier.uri | https://dspace.univ-eloued.dz/handle/123456789/10783 | |
dc.language.iso | en | en_US |
dc.publisher | University of Eloued جامعة الوادي | en_US |
dc.subject | Intelligent Transportation System, Traffic Forecasting, Artificial Neural Network, Deep Learning | en_US |
dc.title | A Comparative Study Of Road Traffic Forecasting Models | en_US |
dc.type | Other | en_US |
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