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    This study introduces CSLens, a visual analytics system to optimize electric vehicle (EV) charging station placement. CSLens holistically assesses impacts on transportation and power grids, improving infrastructure planning.

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    Area of Science:

    • Transportation Engineering
    • Computer Science
    • Energy Systems

    Background:

    • Global electric vehicle (EV) adoption is rapidly increasing, necessitating efficient charging station deployment.
    • Existing charging station location problem (CSLP) algorithms face limitations in spatiotemporal evaluation and computational efficiency.
    • Charging station placement significantly impacts interconnected road and power networks, requiring holistic assessment.

    Purpose of the Study:

    • To develop a visual analytics system, CSLens, for informed electric vehicle charging station deployment decisions.
    • To enable users to analyze existing layouts, explore alternatives, and evaluate impacts on coupled networks.
    • To address the limitations of current CSLP algorithms by providing comprehensive spatiotemporal and network impact evaluations.

    Main Methods:

    • Development of CSLens, a visual analytics system integrating transportation and power network data.
    • Implementation of interactive visualizations and analytical features for deployment scenario exploration.
    • Validation through case studies and domain expert interviews.

    Main Results:

    • CSLens provides a holistic view of charging station deployment impacts on transportation and power grids.
    • The system facilitates the exploration of alternative deployment strategies and their consequences.
    • Case studies and expert feedback confirmed the usability and practical utility of CSLens.

    Conclusions:

    • CSLens enhances decision-making for electric vehicle charging infrastructure planning.
    • The system offers a valuable tool for navigating the complexities of deploying charging stations.
    • CSLens demonstrates the potential for visual analytics in optimizing sustainable transportation systems.