TRB 2016 Blue Ribbon Committee
16th National Transportation Planning Applications Conference

Autonomous Vehicle Ownership And Sharing: A Demand Forecasting Approach For The Puget Sound Region And Beyond

Corresponding Author: Chandra Bhat, The University of Texas

Presented By: Chandra Bhat, The University of Texas


Autonomous vehicles (AV) are no longer a futuristic idea. The closer this technology gets to mass production, the more urgent it becomes for us to understand public perceptions and willingness to adopt it. This study describes an effort to quantify the potential initial demand for AV technology in the Puget Sound Region (PSR) of the State of Washington in the United States. The forecasting process was divided into two phases. First, we developed a behavioral framework for AV adoption interest based on latent psychological constructs such as environmental consciousness, technology-savviness, and AV-apprehensiveness. This behavioral framework was then translated into an integrated choice and latent variable model that used data from the 2014-2015 Puget Sound Regional Travel Study as well as land use data to estimate users’ interests in AV adoption. Two forms of adoption were considered, AV ownership and AV sharing, and individuals were grouped into one of four different categories: interested in AV ownership, interested in AV sharing, interested in both options, and not interested in AV adoption. Second, we used Census data of the same region to generate a synthetic population for each Census Block Group and, based on that population, we predicted how the different demands would be geographically distributed across the PSR. This prediction is then translated into an interactive visualization of the results.

The map resulting from our analysis is an important resource for the different stakeholders involved in planning and implementing the future of transportation. For instance, transportation authorities can use these results to identify areas of interest for analyzing AV deployment impacts under alternative future scenarios. On the other hand, mobility providers of ride-sourcing services (such as Uber and Lyft), and carsharing services (such as ZipCar and Car2go), may use such results to identify effective spatial strategies for deploying shared AV systems. Finally, since Census data is available for all cities in the U.S., based on behavioral transferability assumptions, the model developed for the PSR may be transferred to produce initial forecasts of AV adoption interest for different cities in the country.


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