Private or on-demand autonomous vehicles?Modeling public interest using a multivariate model

With the likely future of autonomous vehicles(AVs)as private,ride-hailing,and pooled vehicles,it is important to consider all forms of AVs when estimating the impacts of automation on travel behavior.To aid this,this study jointly models the public interest in three forms of AVs(owning,ride-hailing,and using pooled services)and compares the interests in owning versus ride-hailing AVs using a combination of structural equation modeling and multivariate ordered probit modeling frameworks.Using the 2019 California Vehicle Survey data,we estimate the impacts of several exogenous and latent variables on all forms of AV adoption.We find that the individual,household,travel-related,and built-environment factors are related to different forms of AV adoption directly and indirectly through attitudes toward human and automated driving.We also report that human and automated driving sentiments have the highest impact on interest in owning an AV compared to interest in ride-hailing and using pooled AVs.We discuss several policy implications by calculating the pseudo-elasticity effects of exogenous variables and the sensitivities of the impacts on latent variables on different forms of AV adoption.For example,public interest in owning private AVs can be increased by more than 7%by making them familiar with autonomous technology.

A copula-based approach to accommodate intra-household interaction in workers' daily maintenance activity stop generation modeling

A new stop frequency model was developed to predict the daily number of maintenance activity stops made by individual household heads during a typical weekday. This new model was based on the modification of a conventional multivariate ordered probit(MOP) model by maintaining the probit assumption for the marginal distributions while introducing nonnormal dependence among the error terms using copula functions. Therefore, the copulabased MOP model would relieve the restriction of imposing joint normality on the error terms in the conventional MOP model. The new MOP model would not only account for the intrahousehold interactions in stop-making decisions, but also allow the best functional form to be determined for representing dependencies among household heads. Using the New York Metropolitan Transportation Council’s 2010/2011 regional household travel survey data, the copula-based MOP model was employed to examine stop-making behavior for individual household heads residing in New York City and its adjacent counties in Mid-Hudson Valley and New Jersey. Empirical results provided useful insights into the observed effects of sociodemographics, land use density, transportation service, and work schedule together with potential unobserved common effects on the inter-relatedness of spousal stop-making decisions at the household level. The results show that the MOP model with a Clayton copula structure provides the best data fits and there is a very strong positive dependence among error terms of stop-making equations. Furthermore, the dependence among the maintenance activity propensities of household heads is asymmetric, with a stronger tendency of household heads to simultaneously have low maintenance activity levels than to simultaneously have high maintenance activity levels.