Results from a recent consumer survey were thoroughly analyzed in relation to willingness to adopt and willingness to pay (WTP) for different autonomous vehicle (AV) features. Four different levels of automation were considered including basic vehicles, adding advanced features, partial automation, and full automation. A structural equations model with latent variables was employed, which simultaneously regressed adoption and WTP levels against a variety of available variables including socioeconomic and demographic attributes, private car usage habits, and attitudinal preferences/personal opinions. To address the endogeneity in personal attitudes, these variables were added to the model as latent factors. Accordingly, the analysis revealed four major latent attitudinal factors, respectively labeled as “joy of driving,”“mode choice reasoning,”“trust,” and “technology savviness.” Model results indicated that those who enjoy driving were the hardest to persuade towards AV adoption or to pay for automated features. On the other hand, technology savvy people showed higher tendency towards AV adoption. When it comes to factors affecting mode choice including travel time, travel cost, and functionality, people are willing to pay more for automated features when they believe that these features and services will provide them better utility, in relation to time and cost savings, convenience, stress reduction, and quality of life, and so forth. Interestingly, individuals with trust concerns showed higher WTP values, which may indicate that the market believes autonomous vehicles will bring more privacy and protection, at least compared with existing shared mobility or public transit options.
How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.
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