Lopes Rettore, Paulo HenriquePaulo HenriqueLopes RettoreLopes, Roberto Rigolin F.Roberto Rigolin F.LopesMaia, GuilhermeGuilhermeMaiaAparecido Villas, LeandroLeandroAparecido VillasFerreira Loureiro, Antonio AlfredoAntonio AlfredoFerreira Loureiro2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/41107110.1109/DCOSS.2019.00106In this work, we propose Traffic Data Enrichment Sensor (TraDES), towards a low-cost traffic sensor for Intelligent Transportation System (ITS) based on heterogeneous data fusion. TraDES aims at fusing data from vehicular traces with road traffic data to enrich current spatiotemporal traffic data. In that direction, we propose a robust methodology to group spatially and temporally these different data sources, producing a vehicular trace with its respective traffic conditions, which is given as input to a learning-based model based on Artificial Neural Networks (ANN). Hence, TraDES is an enriched traffic sensor that is able to sense (detect) traffic conditions using a scalable and low-cost approach and to increase the spatiotemporal traffic data coverage.en004conference paper