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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Multisource Data Framework for Road Traffic State Estimation


    Beteiligte:


    Erscheinungsdatum :

    2018




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Multisource Data Framework for Road Traffic State Estimation

    Grau, J.M.S. / Mitsakis, E. / Tzenos, P. et al. | Fraunhofer Publica | 2018

    Freier Zugriff

    Freeway Traffic State Estimation Method Based on Multisource Data

    Shang, Ying / Li, Xingang / Jia, Bin et al. | ASCE | 2022



    An Approach to Urban Traffic State Estimation by Fusing Multisource Information

    Qing-Jie Kong, / Zhipeng Li, / Yikai Chen, et al. | IEEE | 2009