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19 octobre 2017

[IFSTTAR-MLV] Traffic flow prediction with Deep Learning

Catégorie : Stagiaire

6 Months internship at IFSTTAR - RER A - Noisy/Champ (France)

Laboratory: GRETTIA

contact: rachid.belaroussi@ifsttar.fr

Maitrise du C++ ou de Python indispensable. Niveau M2.

Gratification 25,60€/jour (7h/jour) + 50% frais de transport.


GRETTIA lab wishes to investigate the potential of deep learning algorithms applied to short-term traffic flow prediction. Smart highway systems are commonly monitored using sensing technologies such as inductive loop buried in the pavement: they record the number of vehicles, and their speed, passing over the loop during a unit of time. In the case of the Caltrans Performance Measurement System (PEMS), data are collected from nearly 40000 individual detectors spanning the freeway system across all major metropolitan areas of California: over ten years of data are archived, with a variety of information (traffic detectors, speed estimation, incidents, lane closures, census traffic counts, toll tags…). Traffic flow (or volume) over a period of 15 minutes is expressed in Vehicles-15 minutes.


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