The valuation and demand impacts of the worthwhile use of travel time with specific reference to the digital revolution and endogeneity

Wardman, Mark; Chintakayala, Phani Kumar; Heywood, Christopher

doi:10.1007/s11116-019-10059-x

Cited by 25 publications

(12 citation statements)

References 30 publications

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“…As for the link between travel-based multitasking and value of travel time, it is worth observing that a number of studies such as Malokin et al (2019) have postulated that the activities undertaken while travelling can have impact on mode choice decisions. This coincides with the findings of (Wardman et al 2019;Molin et al 2020), in relation to downrating of the value of travel time (VoT) savings (as the value of time spent travelling increases), especially through the digital age in which mobile ICTs decouple activities from their previous particular spatial and temporal contexts, i.e. association with specific places or times.…”

Section: Literature Reviewsupporting

confidence: 85%

“…More fundamentally, a number of recent studies have explicitly linked the ability to conduct activities while travelling to changes in the value of travel time (Wardman et al 2019;Molin et al 2020). Such evidence helps build a case for the inclusion of such considerations in frameworks relying on the concepts of value of travel time (VTT) and value of (business) travel time savings, or V(B)TTS, 1 including mode choice models or investment appraisal.…”

Section: Introductionmentioning

confidence: 99%

“…Through its subsequent extension and formalisation (Fowkes et al 1986;Batley 2015), it led to emergence of an approach to valuation of travel time savings, the so-called Hensher Equation (HE), that does account for the use of travel time for work-related activities. Despite its apparent attractiveness, it has been employed in only a limited number of cases and typically in a restricted form due to the difficulty of fully populating the model with data (Wardman et al 2019). In addition, the HE is focussed upon business travellers and their productivity, neglecting the typically larger segment of non-business passengers.…”

Section: Introductionmentioning

confidence: 99%

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Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel

2021

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Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation.

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Section: Literature Reviewsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel

2021

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“…For example, people may use time more productively (either in vehicle or out of vehicle) or travel to more distant places. Some research has investigated ways of incorporating the overlay of activities while traveling into conventional models of time allocation (Pudāne et al, 2018), value of travel time savings (Wardman et al, 2019), or mode choice (Malokin et al, 2019), while at least one study examined the potential willingness to commute farther (Olsen and Sweet, 2019). However, our understanding of how AVs will reshape people's (travel-related) activities is still limited.…”

Section: Introductionmentioning

confidence: 99%

Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia

Kim

Circella

2020

Transportation Research Part D: Transport and Environment

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Many studies have begun investigating possible transportation landscapes in the autonomous vehicle (AV) era, but empirical results on longer-term decisions are limited. We address this gap using data collected from a survey designed and implemented for Georgia residents in 2017-2018. Focusing on a hypothetical all-AV future, this section of the survey included questions regarding advantages/disadvantages of AVs, short-term mode choice impacts, mediumterm impacts on activity patterns, and long-term behavioral changes-specifically, whether/how AVs will influence individuals to change residential location and the number of cars in the household. We hypothesize that AVs could act in concert with attitudinal preferences to stimulate changes in these long-term decisions, and that some medium-term activity changes triggered by AVs could motivate people to relocate their residence or shed household vehicles. We applied exploratory factor analysis to measure the perceived likelihood that AVs would prompt various medium-term changes. We then included some of those measures, among other variables, in a cross-nested logit (CNL) model of the choice of the residential location/vehicle ownership bundle. Although more than half of respondents expected "no change" in their bundle, we found that younger, lower income, pro-suburban, and pro-non-car-mode individuals were more likely to anticipate changing their selections. In addition, some expected medium-term impacts of AVs influenced changes in these longer-term choices. We further applied the CNL model to two population segments (Atlanta and non-Atlanta-region residents). We found notable improvement in goodness of fit and different effects of factors across segments, signifying the existence of geography-related taste heterogeneity.

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“…VTT is the difference between the opportunity value of travel time (the utility which could be produced if the time were spent for other activities) and the value created while travelling (DeSerpa 1971;Kouwenhoven and de Jong 2018). The possibility to engage in other activities (such as working, relaxing, exercising) during the travel time increases the value created while travelling, resulting in net VTT savings (Kouwenhoven and de Jong 2018; Le et al 2020;Wardman et al 2020). The digital revolution has enabled the productive use of travel time in an unprecedented manner.…”

Section: Introductionmentioning

confidence: 99%

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

et al. 2021

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Automated vehicles (AVs) are expected to change personal mobility in the near future. Most studies on the mobility impacts of AVs focus on fully automated (SAE L5) vehicles, but the gradual development of the technology will probably bring AVs with more limited capabilities to begin with. This stated-preference study focused on the potential mobility impacts of conditionally automated (L3) and highly automated cars (L4). We investigated personal mobility impacts among 59 participants who experienced automated driving repeatedly in a driving simulator. Half of them drove with an L3 and half with an L4 motorway function. After the first and final drive they answered questions on their travel experience and how automated vehicles could change their mobility. After the drives, participants in both groups were willing to accept 30–50% longer travel times for a 30 min trip if they did not need to drive the whole trip themselves. This translates into savings of around 30% for the perceived value of travel time on routes where automation is available. There were no statistically significant differences between L3 and L4 in the accepted travel times. Most participants did not expect to make more trips with automated cars, but around half of them anticipated making longer trips. The amount of car travel may increase more with L4 than with L3 automation, possibly due somewhat to changes in the experienced travel quality. The results suggest that the mobility impacts of automated driving may increase with a higher level of automation.

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The valuation and demand impacts of the worthwhile use of travel time with specific reference to the digital revolution and endogeneity

Cited by 25 publications

References 30 publications

Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel

Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel

Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

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