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Multisource Data Framework for Road Traffic State Estimation

: Grau, J.M.S.; Mitsakis, E.; Tzenos, P.; Stamos, I.; Selmi, L.; Aifadopoulou, G.

Fulltext ()

Journal of advanced transportation 2018 (2018), Art. 9078547, 9 pp.
ISSN: 0197-6729
ISSN: 2042-3195
Journal Article, Electronic Publication
Fraunhofer IAIS ()

This paper presents a framework for data collection, filtering, and fusion, together with a set of operational tools to validate, analyze, utilize, and highlight the added value of probe data. Data is collected by both conventional (loops, radars, and cameras) and innovative (Floating Car Data, detectors of Bluetooth devices) technologies and refers to travel times and traffic flows on road networks. The city of Thessaloniki, Greece, serves as a case study for the implementation of the proposed framework. The methodology includes the estimation of traffic flow based on measured travel time along predefined routes and short-term forecasting of traffic volumes and their spatial expansion in the road network. The proposed processes and the framework itself have the potential of being implemented in urban road networks.