Traffic state prediction services for automated driving and traffic management.

Luc J.J. Wismans, Henri Palm, Han Zwijnenberg, Etiene Wieme

    Research output: Contribution to conferencePaperAcademic

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    The target in the PRYSTINE project is to realize Fail-operational Urban Surround perceptION (FUSION), which is based on sensor fusion, and control functions in order to enable safe automated driving in urban and rural environments. Estimation of the complete current and (near) future traffic conditions ahead, beyond the range of on-board vehicle sensors, provides the automated driving controller with enhanced information to act better and more comfortably in the current situation and to extent road safety. Traffic state prediction is also an important input for pro-active traffic management as identified within TM2.0 (Traffic Management2.0 vision ERTICO). The derivation of a common
    operational picture for traffic management and mobility service providers, like CAV, enables the collaboration between public and private parties in facilitating traffic. Stimulating and enhancing this collaboration is part of the Dutch innovation program MobilitymoveZ. Significant improvements of quality and availability of data offers the opportunity to provide such information. By combining data science and traffic modelling techniques, an application is developed consisting of current and short term traffic prediction (typically up to 10 minutes ahead) and a virtual patrol detecting congestion and incidents for urban and non-urban networks.
    Original languageEnglish
    Publication statusPublished - 9 Oct 2019
    Event47th European Transport Conference, ETC 2019 - Dublin Casle, Dublin, Ireland
    Duration: 9 Oct 201911 Oct 2019


    Conference47th European Transport Conference, ETC 2019
    Abbreviated titleETC
    Internet address

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