Highlights Study shows spatial clustering and peer effects in electric vehicle (EV) ownership. More EV owners are from higher income households residing closer to city center. Families with non-workers (e.g. children and elderly persons) are less likely to own EVs. Spatial count model accounts for both spatially-lagged and aspatial correlations.

    Abstract Electric vehicles (EVs) are predicted to increase in market share as auto manufacturers introduce more fuel efficient vehicles to meet stricter fuel economy mandates and fossil fuel costs remain unpredictable. Reflecting spatial autocorrelation while controlling for a variety of demographic and locational (e.g., built environment) attributes, the zone-level spatial count model in this paper offers valuable information for power providers and charging station location decisions. By anticipating over 745,000 personal-vehicle registrations across a sample of 1000 census block groups in the Philadelphia region, a trivariate Poisson-lognormal conditional autoregressive (CAR) model anticipates Prius hybrid EV, other EV, and conventional vehicle ownership levels.Initial results signal higher EV ownership rates in more central zones with higher household incomes, along with significant residual spatial autocorrelation, suggesting that spatially-correlated latent variables and/or peer (neighbor) effects on purchase decisions are present. Such data sets will become more comprehensive and informative as EV market shares rise. This work’s multivariate Poisson-lognormal CAR modeling approach offers a rigorous, behaviorally-defensible framework for spatial patterns in choice behavior.


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    Titel :

    Where are the electric vehicles? A spatial model for vehicle-choice count data


    Beteiligte:
    Chen, T. Donna (Autor:in) / Wang, Yiyi (Autor:in) / Kockelman, Kara M. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2015-01-01


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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