Under CopyrightRoscher, KarstenKarstenRoscherNitsche, ThomasThomasNitscheKnorr, RudiRudiKnorr2022-03-1328.2.20182017https://publica.fraunhofer.de/handle/publica/39987310.24406/publica-r-39987310.1109/VTCFall.2017.8288303Current advances in vehicular ad-hoc networks (VANETs) point out the importance of multi-hop message dissemination. For this type of communication, the selection of neighboring nodes with stable links is vital. In this work, we address the neighbor selection problem with a data-driven approach. To this aim, we apply machine learning techniques to a massive data-set of ETSI ITS message exchange samples, obtained from simulated traffic in the highly detailed Luxembourg SUMO Traffic (LuST) Scenario. As a result, we present classification methods that increase neighbor selection accuracy by up to 43% compared to the state of the art.enclassificationfeature selectionlink estimationmachine learningneighbor selectionneural networksimulationvehicular ad hoc networkVANETV2XVehicle-to-XunicaststandardreliabilityestimationCamsadaptation model621conference paper