In the rapidly evolving domain of electric vehicle (EV) usage and infrastructure development, the paucity of comprehensive and detailed data poses significant challenges for effective planning and implementation. This study proposes an innovative framework for synthesizing and fusing data to construct comprehensive synthetic datasets that can assist with policy-making at different stages of EV adoption. The framework comprises a Data Generation module, leveraging Sequential Generative Adversarial Networks to generate survey and travel sequence data, and a Data Fusion module to integrate the synthetic data, resulting in profiles that combine socio-demographic attributes with travel behaviors. By utilizing data from Indiana state, USA, as a case study, the research generates synthetic datasets that reflect realistic household and travel behaviors. The usefulness of the dataset is demonstrated by one sample application that estimates EV penetration rates in the next few years under a range of future scenarios. This innovative method bridges the existing gap in data availability, significantly enhancing our capacity to analyze EV usage patterns. The application of this framework is instrumental for policymakers and urban planners, offering a sophisticated tool to guide the strategic development of EV infrastructure and support the transition towards sustainable mobility.


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    Title :

    Estimation of Electric Vehicle Adoption Rates Using Sequential Generative Adversarial Networks




    Publication date :

    2024-09-24


    Size :

    723233 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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