Real-Time Traffic Data Smoothing from GPS Sparse Measures Using Fuzzy Switching Linear Models

Abstract : Traffic is one of the urban phenomena that have been attracting substantial interest in different scientific and industrial communities since many decades. Indeed, traffic congestions can have severe negative effects on people's safety, daily activities and quality of life, resulting into economical, environmental and health burden for both governments and organizations. Traffic monitoring has become a hot multidisciplinary research topic that aims to minimize traffic's negative effects by developing intelligent techniques for accurate traffic states' estimation, control and prediction. In this paper, we propose a novel algorithm for traffic state estimation from GPS data and using fuzzy switching linear models. The use of fuzzy switches allows the representation of intermediate traffic states, which provides more accurate traffic estimation compared to the traditional hard switching models, and consequently enables making better proactive and in-time decisions. The proposed algorithm has been tested on open traffic datasets collected in England, 2014. The results of the experiments are promising, with a maximum absolute relative error equal to 9.04%.
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Contributor : Stéphane Derrode <>
Submitted on : Sunday, August 6, 2017 - 3:06:32 PM
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Zied Bouyahia, Hedi Haddad, Nafaâ Jabeur, Stéphane Derrode. Real-Time Traffic Data Smoothing from GPS Sparse Measures Using Fuzzy Switching Linear Models. The 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017), Jul 2017, Leuven, Belgium. pp.143-150, ⟨10.1016/j.procs.2017.06.136⟩. ⟨hal-01572285⟩

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