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.
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Benabdallah Benarmas, R., Beghdad Bey, K. (2022). A Comparative Study of Road Traffic Forecasting Models. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_25
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DOI: https://doi.org/10.1007/978-3-030-96311-8_25
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