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Towards a Traffic Data Enrichment Sensor Based on Heterogeneous Data Fusion for ITS

: Lopes Rettore, Paulo Henrique; Lopes, Roberto Rigolin F.; Maia, Guilherme; Aparecido Villas, Leandro; Ferreira Loureiro, Antonio Alfredo


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
15th International Conference on Distributed Computing in Sensor Systems, DCOSS 2019. Proceedings : 29-31 May 2019, Santorini Island, Greece
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0570-3
ISBN: 978-1-7281-0571-0
International Conference on Distributed Computing in Sensor Systems (DCOSS) <15, 2019, Santorini Island>
Conference Paper
Fraunhofer FKIE ()

In 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.