On short-term traffic flow forecasting and its reliability

Hassane Abouaïssa (), Michel Fliess () and Cédric Join ()
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Hassane Abouaïssa: LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois - UA - Université d'Artois
Michel Fliess: LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, ALIEN
Cédric Join: CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, NON-A - Non-Asymptotic estimation for online systems - Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique - CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 - Centrale Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, ALIEN

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Abstract: Recent advances in time series, where deterministic and stochastic modelings as well as the storage and analysis of big data are useless, permit a new approach to short-term traffic flow forecasting and to its reliability, i.e., to the traffic volatility. Several convincing computer simulations, which utilize concrete data, are presented and discussed.

Keywords: forecasts; financial engineering; time series; management systems; intelligent knowledge-based systems; road traffic; transportation control; persistence; risk; volatility (search for similar items in EconPapers)
Date: 2016-06-28
New Economics Papers: this item is included in nep-cmp, nep-for and nep-tre
Note: View the original document on HAL open archive server: https://hal.science/hal-01275311v2
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Citations: View citations in EconPapers (2)

Published in 8th IFAC Conference on Manufacturing Modelling, Management & Control, MIM 2016, Jun 2016, Troyes, France

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