, Handle Message : Received Event Description Message 2: check and cast < EDM > ; 3: Go to line 10, Handle Message : Predict the Traffic Information, vol.4

, 13: end if 14: if the collected data are available 15: Predict the traffic information, Handle Message : Send Traffic Information 19: if the traffic information are convenient for On-demand model, vol.16

, 21: end if 22: if the traffic information are convenient for all vehicles 23: wait until the elapsed time of the broadcast = true, vol.24

, Send EDvM to intelligent cloud services to control the vehicles mobility. 28: end if 29: if the traffic information is convenient for serious situations 30: Send traffic report to the traffic management center, 25: end if 26: if the traffic information are convenient for, vol.27

, ODM.VehPosition = getVehGPSPosition(

, ODM.On-demandData = getOn-demandData(

, Send Event Description Message 8: Create and initiate < EDM > 9: EDM.VehicleIndex = getVehicleID

, EDM.EventPosition = getEvtPosition(, vol.10

, EDM.EvtDescription = getEvtDescriptionData(, vol.12

, Handle Message : Receive Downlink Data Collection Message 15: Create and initiate < UDC > 16: UDC, VehicleIndex = getVehicleID(, vol.14

, UDC.VehiclePosition = getVehGPSPosition(, vol.20

, Handle Message : Receive Periodic Message 25: check and cast < PM > 26: Update local data le trafic seront envoyées sous forme de, Handle Message : Receive Traffic Information Message 22: check and cast < TIM, vol.21

, Simulation et évaluation

. Nunez, 2012) pour évaluer la performance proposée en intégrant le mécanisme de prédiction et d'échange de données. De cette manière, la description du scénario est basée sur l'hypothèse montrée dans la figure 5.5. Les véhicules sont équipés d'une large gamme de dispositifs embarqués, tels que des capteurs connectés (RFID, caméra connectée, détection d'événement de véhicule), Au cours des dernières décennies, le nombre et l'ampleur des embouteillages ont augmenté de façon exponentielle en Algérie. Un simple voyage peut prendre des heures, surtout dans les grandes villes et les environs

, La figure 5.6 montre le modèle de flux du trafic utilisé pour évaluer le modèle de prédiction, où les données sont enregistrées tout au long de la journée en utilisant des détecteurs de boucle

, Une fois le message EDM reçu au service Cloud, l'événement 2 (Evt2 : Send Downlink Data Collection Message) est déclenché pour prédire l'état réel du réseau du trafic en utilisant les données collectées par les véhicules et les détecteurs. Cependant, les véhicules impliqués dans l'événement 3 (Evt3 : Sending Uplink Data Collection Message) vont générer chacun un message UDC qui comprend les données contextuelles suivantes : VehicleIndex, VehicleSource, VehicleDestination et VehiclePosition. Lors de l'arrivée de l'événement 4 (Evt4 : Sending Traffic Information), le service Cloud va diffuser les données prédites de chaque véhicule en

, Liste Des Publications de Nos Travaux

S. Abdelatif, D. Makhlouf, and P. Roose, Smart Traffic Management System for Anticipating Unexpected Road Incidents in Intelligent Transportation Systems, International Journal of Grid and High Performance Computing (IJGHPC), vol.10, issue.4, 2018.

, ? Conférences internationales avec comité de lecture

S. Abdelatif and D. Makhlouf, Philippe Roose -Extended iCanCloud simulation framework for VANET-Cloud architectures, 3rd International Conference on Networking and Advanced Systems -13-14, 2017.

S. Abdelatif, D. Makhlouf, P. Roose, and D. Becktache, Loop Speed Trap Data Collection Method for an Accurate Short-Term Traffic Flow Forecasting, International Conference on Mobile Web and Information Systems, pp.56-64, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02437024

S. Abdelatif, D. Makhlouf, P. Roose, and D. Becktache, An Intelligent Architecture for VANET-Cloud Computing -PAIS, 2016.

S. Abdelatif, D. Makhlouf, and D. Becktache, Réseau pour l'informatique de nuage véhiculaire, Journnées Ouvertes sur les Mathématiques et l'Informatique (JOMI'2015)

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