Vehicular-cloud simulation framework for predicting traffic flow data. (20th January 2020)

• Record Type: Journal Article
• Authors: Abdelatif, Sahraoui; Makhlouf, Derdour; Ahmim, Ahmed; Roose, Philippe
• Is Part Of: International journal of internet technology and secured transactions. Volume 10:Number 1/2(2020)
• Page Start: 102
• Page End: 119
• Publication Date: 2020-01-20
• Subjects: traffic data -- iCanCloud framework -- data prediction -- vehicular network; Internet -- Security measures -- Periodicals; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1748-569X

Abstract

The traffic flow prediction has become an important process tailored with the exponential development of cities and the transportation systems. The main purpose of the prediction task is to improve the logistic services and reduce the cost of the road congestion. In this paper, we propose a vehicular-cloud simulation framework with a layer of traffic cloud services to predict accurate traffic flow data. Learning of supervised traffic flow data from several data sources is the core of these services. Particularly, we focus on a particular type of dependency (i.e., monotone dependency) between the learning traffic inputs and its responses. The learning algorithm we propose aims to solve the regression problem by predicting values of a continuous measure. The accuracy of the proposed cloud services have been tested under congestion conditions, where the results show better performances over short periods and daily forecasts.