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RCMVis: A Visual Analytics System for Route Choice Modeling.

DongHwa Shin, Jaemin Jo, Bohyoung Kim

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    |December 1, 2021
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    RCMVis, a visual analytics system, helps analyze traveler route choice behaviors by modeling route characteristics like distance. Domain experts gained new insights and refined models more effectively.

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    Area of Science:

    • Transportation Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Route choice modeling is crucial for understanding traveler behavior.
    • Existing methods often lack interactive exploration and refinement capabilities.
    • Visual analytics can enhance the analysis of complex transportation data.

    Purpose of the Study:

    • To introduce RCMVis, a visual analytics system for interactive Route Choice Modeling.
    • To support analysts in exploring, modeling, and reasoning about traveler route choices.
    • To facilitate the identification of route characteristics influencing travel decisions.

    Main Methods:

    • Developed a visual analytics framework with three stages: exploration, modeling, and reasoning.
    • Integrated coordinated multiple origin-destination (OD) views for data exploration.
    • Incorporated k-medoids clustering and path-size logit models for behavior analysis.
    • Enabled feature selection, hyperparameter tuning, and model comparison.

    Main Results:

    • RCMVis allows interactive exploration and selection of trip data subsets.
    • The system supports robust route choice modeling with integrated algorithms.
    • Domain experts discovered unexpected insights and refined models efficiently.
    • Gained OD- and road-level insights supporting model validation and discussion.

    Conclusions:

    • RCMVis effectively supports interactive Route Choice Modeling analysis.
    • The system enhances the ability of analysts to explore complex travel data and refine models.
    • Visual analytics provides valuable tools for understanding and improving transportation models.