This paper is intended to mine historical data by presenting a scenario clustering approach to identify appropriate scenarios for mesoscopic simulation as a part of the evaluation of transportation projects or operational measures. It provides a systematic and efficient approach to select and prepare effective input scenarios for a given traffic simulation model. The scenario clustering procedure has two primary applications: travel time reliability analysis, and traffic estimation and prediction systems. The ability to systematically identify similarity and dissimilarity among weather scenarios can facilitate the selection of critical scenarios for reliability studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current or predicted weather condition into predefined categories and suggesting relevant WRTM strategies that can be tested via real-time traffic simulation before deployment. A detailed method for clustering weather time series data is presented and demonstrated using historical data. Two clustering algorithms with different similarity measures are compared. Clustering results using a k -means clustering algorithm with squared Euclidean distance are illustrated in the travel time reliability application.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Operational Scenario Definition in Traffic Simulation-Based Decision Support Systems: Pattern Recognition Using a Clustering Algorithm


    Contributors:


    Publication date :

    2019-02-08




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown



    Thermal face recognition in an operational scenario

    Socolinsky, D.A. / Selinger, A. | IEEE | 2004


    Thermal Face Recognition in an Operational Scenario

    Socolinsky, D. / Selinger, A. / IEEE Computer Society | British Library Conference Proceedings | 2004


    GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

    Hasan, Md Mehedi / Oh, Jun-Seok | Transportation Research Record | 2020



    TRAFFIC SCENARIO CLUSTERING BY ITERATIVE OPTIMISATION OF SELF-SUPERVISED NETWORKS USING A RANDOM FOREST ACTIVATION PATTERN SIMILARITY

    Balasubramanian, Lakshman / Wurst, Jonas / Botsch, Michael et al. | British Library Conference Proceedings | 2021