Fully automated vehicles: the use of travel time and its association with intention to use

Abstract

Traditionally, time spent travelling has been seen as a ‘cost’ to the traveller. Autonomous or fully automated vehicles (FAVs) can free the driver of the driving task and allow engagement in other worthwhile activities inside the FAVs, which can transform how people travel. However, there is little understanding about how travel time can be used and how worthwhile this time can be in FAVs; and whether this is related to the intention to use FAVs. This paper addresses these questions through a multi-country questionnaire survey, with a sub-sample of chauffeur-driven car users to mimic time use in FAVs. Responses show that users are likely to engage in other non-driving activities while riding in FAVs, and these differ according to trip purpose and direction. Time spent travelling in FAVs is perceived to be more useful than in current modes of transport. Interest in using FAVs is directly correlated with perceived usefulness of time in autonomous vehicles. There is a strong correlation between intended activities in FAVs and current activities by primary car users in chauffeur-driven cars, providing some validation to the stated intention responses. Results have important implications for policy-making, time use and value-of-time research, as well as vehicle interior design.

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

“…According to Flügel et al (2019), the reduction for the VOT in AV is 21% on average, with a greater impact for full automation compared to partial automation and more perceived benefits in privatelyowned AV as in shared ones. Wadud and Huda (2019) correlate the perceived usefulness of travel time in autonomous vehicles with the activities that people may engage in. Their findings also indicate that, besides the trip purpose, the direction (driving to work vs. driving from work back home) influences desirable non-driving activities while riding in an AV.…”

The complexity of value of travel time for self-driving vehicles – a morphological analysis

“…Due to lack of empirical data from real-world SDV implementations, most of the current research is based on stated preference surveys, which are vulnerable to hypothetical bias. To address this, some authors compare stated preference for SDVs with actual usage of chauffeur driven cars (Wadud and Huda 2019). While some work relates to shared SDVs (Chen and Kockelman 2016), the actual characteristics of the travel mode do not seem to be taken into consideration.…”

Potential health and well-being implications of autonomous vehicles

“…Of course, the high expense associated with these types of experiments makes it difficult to obtain large sample sizes, negatively affecting representativeness. Another promising approach is to research travel behavior in locations where chauffeur-driven cars are commonplace (Wadud and Huda, 2019). As analog for shared/pooled AVs, the impacts of ride-hailing services on travel behaviors and mode shifts could be studied (e.g., Alemi et al, 2018;Clewlow and Mishra, 2017).…”