A Time-use Model for the Automated Vehicle-era. (August 2018)

• Record Type: Journal Article
• Authors: Pudāne, Baiba; Molin, Eric J.E.; Arentze, Theo A.; Maknoon, Yousef; Chorus, Caspar G.
• Is Part Of: Transportation research. Volume 93(2018)
• Page Start: 102
• Page End: 114
• Publication Date: 2018-08
• Subjects: Automated vehicles -- Time-use model -- On-board activities -- Travel behaviour -- Travel demand -- Multitasking; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.05.022
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Travel time penalty is insufficient to model travel choices in the AV-era. We propose a model that captures time-use effects of on-board activities. We test the face validity of our model and apply it to extended examples. Abstract: Automated Vehicles (AVs) offer their users a possibility to perform new non-driving activities while being on the way. The effects of this opportunity on travel choices and travel demand have mostly been conceptualised and modelled via a reduced penalty associated with (in-vehicle) travel time. This approach invariably leads to a prediction of more car-travel. However, we argue that reductions in the size of the travel time penalty are only a crude proxy for the variety of changes in time-use and travel patterns that are likely to occur at the advent of AVs. For example, performing activities in an AV can save time and in this way enable the execution of other activities within a day. Activities in an AV may also eliminate or generate a need for some other activities and travel. This may lead to an increase, or decrease in travel time, depending on the traveller's preferences, schedule, and local accessibility. Neglecting these dynamics is likely to bias forecasts of travel demand and travel behaviour in the AV-era. In this paper, we present an optimisation model which rigorously captures the time-use effects of travellers' ability to perform on-board activities. Using a series of worked out examples, we test the face validity of the model and demonstrate how it can be used to predict travel choices in the AV-era.