Publication Detail

A First Look at Vehicle Miles Travelled in Partially-Automated Vehicles

UCD-ITS-WP-18-01

Working Paper

Suggested Citation:
Hardman, Scott, Rosaria M. Berliner, Gil Tal (2018) A First Look at Vehicle Miles Travelled in Partially-Automated Vehicles. Institute of Transportation Studies, University of California, Davis, Working Paper UCD-ITS-WP-18-01

This paper contributes to research investigating the impact of automated and partially automated vehicles on travel behavior. This contribution comes from taking a first look at the impact of partially/semi-automated (SAE Level 2) vehicles on travel behavior and potential correlations with vehicle miles travelled (VMT). The results of this study are taken from a questionnaire survey of 3,001 plug-in electric (PEV) owners in the USA, of which 347 own a partially-automated vehicle (e.g Tesla Model S with Autopilot). This study looks at the VMT of different vehicle types in the survey including plug-in hybrids (PHEVs), battery electric vehicles (BEVs), and semi-automated BEVs. This comparison reveals that semi-automated BEVs have significantly higher VMT compared to other vehicle types. Least squares regression is used to understand VMT in semi-automated BEVs further. This reveals a significant relationship between commute distance, age, household income, house type, and the frequency of autopilot use, and annual VMT. It is possible that the results are showing a self-selection causality as owners of these vehicles already drove more prior to them selecting a semi-automated BEV. Nevertheless, this model indicates that as the frequency of autopilot use increases, so does annual VMT. Due to the potential for two ways causality this study cannot determine whether there is a causal relationship between the use of semi-automated vehicle technology and additional VMT. It is hoped that this first look at the impact of partially-automated BEVs will encourage more research and debate in this area with the aim of improving policy responses to partially and fully automated vehicles.