Machine learning applied to network traffic for vehicular applications to predict the vehicle context

The analysis of vehicular traffic and the precise forecast of the user demands enable the development of intelligent vehicular networks. It is usually necessary a considerable effort to obtain, process, and store the data from sensors in vehicles or city infrastructure before using it in benefit of meeting vehicular application's communication requirements. This work deals with the problem of providing information about the vehicular context for the network infrastructure supporting modern vehicular applications. We want to determine whether machine learning (ML) models could be trained on the network measurements of vehicular applications and used to predict events in the vehicular context, such as traffic jams, accidents, among others. We conduct a proof of concept with a typical scenario of an urban traffic jam consisting of vehicles associated with different vehicular applications using the same network infrastructure. The data generated from the network measurements is used to train and test different supervised ML algorithms. The results show that it is possible to use ML applied to the communication network to obtain the vehicular context. The main gain of our proposal is the evaluation of ML applied to the network communication data to predict the vehicular context. This novel approach has the advantage of using data that can be easily obtained in the network core, providing resources that can be used to meet the requirements of modern vehicular applications of Intelligent Transport Systems efficiently.