non-driving activities while travelling, such as working, sleeping, playing games. The impact of this possibility on the satisfaction with travel and on travel demand has been extensively discussed in the literature. However, it has been hardly recognised that the availability of on-board activities influences the (time-geographic) constraints of daily activities and may alter the selection, location, and sequencing of other activities in the day. This hampers correct representation of travel behaviour in activity-based models aiming to predict the effects of AVs on mobility and environment (e.g., greenhouse gas emissions). To help fill this gap, we gathered and analysed qualitative data from focus groups, in which 27 commuters discussed their expectations concerning on-board activities and daily schedules in the AV-era. Among the core insights are the following three. First, it is useful to separate in modelling the satisfaction with travel and the potential for on-board activities during travel: they have different determinants and different consequences for activity schedules and individual travel demand. Second, on-board activities may be classified in 4 quadrants according to their novelty and priority level: this classification is helpful in understanding the potential re-arrangements of daily activities. Third, performing new activities during travel may lead to complex re-arrangements of daily activity patterns; the re-arrangements may ease or also increase time pressure. These, and other reported insights may facilitate more realistic representation of activity-travel behaviour in future travel behaviour models.
|Number of pages||14|
|Journal||Transportation Research Part D: Transport and Environment|
|Publication status||Published - 2018|
- Automated vehicles
- Focus Group
- On-board activities
- Daily activity schedules
- Activity-travel behaviour
- Time pressure
Transcripts of 5 focus groups discussing topic 'Possible impacts of automated vehicles on travel and daily life in the future'
Pudane, B. (Creator), TU Delft - 4TU.Centre for Research Data, 2018