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

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.     

Dense,mixed-use,walkable urban precinct to support sustainable transport or vice versa? A model for consideration from Perth,Western Australia

ABSTRACT

Within the majority of the literature on sustainable transport, it is accepted as ideal to arrange new urban growth in close proximity to major public transit services. While the literature on this subject of transit-oriented developments (TOD) is positive and optimistic, for the most part such assertions are conjectural. This article will attempt to fill this gap by revealing a modeling process undertaken for a local area's reurbanization project to understand the potential and limitations of several modes of transport to support the increased activity density in the precincts. Several of the most standardized policy levers were employed, such as parking ratios and mix of use and building height, and contrasted with the trip generation and transit mode's hourly capacity to reveal potential real-estate yields. The outcomes indicate not only the immediate yields but also the capacity for urban transformation due to each level of sustainable transport investments. The model is unique in that the capacity, parking ratios, and assumptions are highly transparent.