Zou, Tianqi; Khaloei, Moein; Mackenzie, Don. (2020). Effects of Charging Infrastructure Characteristics on Electric Vehicle Preferences of New and Used Car Buyers in the United States. Transportation Research Record, 2674(12), 165 – 175.
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Abstract
The used car market is a critical element for the mass adoption of electric vehicles (EVs). However, most previous studies on EV adoption have focused only on new car markets. This article examines and compares the effects of charging infrastructure characteristics on the preferences for EVs among both new and used car buyers. This study is based on an online stated preference choice experiment among private car owners in the U.S., and the results of comparable binomial logistic models show that new and used car buyers generally share similar patterns in preferences for EVs, with exceptions for sensitivity toward fast charging time, and home charging solutions. Respondents' stated willingness to adopt an EV increases considerably with improvements in driving range, and the effects on new and used car buyers are similar. The study also finds that better availability of charging infrastructure largely increases preference for EVs. The results further reveal that slow and fast charging have complementary effects on encouraging EV adoption as the combination of public slow and fast charging can compensate for the unavailability of home charging.
Zou, Tianqi; Aemmer, Zack; Mackenzie, Don; Laberteaux, Ken. (2022). A Framework for Estimating Commute Accessibility and Adoption of Ridehailing Services Under Functional Improvements from Vehicle Automation. Journal Of Transport Geography, 102.
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Abstract
This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find - unsurprisingly - that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation.
Keywords
Choice Of Transportation; Demand Forecasting; Poor People; Adoption; Price Cutting; Metropolis; Employment Statistics; Los Angeles (calif.); New York (state); Chicago (ill.); Accessibility; Autonomous Vehicles; New Mobility Services; Ridehailing; Travel Demand; Preferences
Sustainable transportation, travel behavior, GIS, geospatial big data