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DTRT13-G-UTC58, Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.

A recent report from the National Highway Traffic Safety Administration indicates that more than 80% of all annual car crashes could be prevented by vehicular communications. To that end, the focus of this project was to develop a framework (conceptualizations, processes, procedures, and algorithms) to harness and mature sensing and communication technology to improve transportation safety, primarily focused on the development of an advanced driver assistance system (ADAS).

TxDOT project number 0-6768, This report discusses the application of the paratransit microsimulation patron accessibility analysis tool, developed by the University of Texas researchers. The research team worked on updating the DRT Accessibility, Tool developed by the Texas Department of Transportation’s Public Transportation Division in Project 5-5178., The primary objectives of this research are (1) classifying the rural transit districts into five distinct categories, (2) updating the DRT Accessibility Tool to make it customizable for each of the five categories, and (3), developing “what if” scenarios for the tool to test. The customization of the tool will make it functional for, applications in all the rural transit districts in Texas.

DTRT13-G-UTC58, This paper develops a microeconomic theory-based multiple discrete continuous choice model that considers: (a) that both goods consumption and time allocations (to work and non-work activities) enter separately as decision variables in the utility function, (b) that both time and money budget constraints govern the activity participation and goods consumption decisions, (c) a finite probability of zero consumptions and zero time allocations (i.e., corner solutions), and (d) technical constraints in the form of minimum consumption levels for any good that is consumed and minimum time allocation for any activity conducted. The time allocation utility function includes time assigned to work also as a decision variable, along with time allocations to various non-work activities, to capture trade-offs between the generation of income for consuming goods and time allocation to non-work activities. The proposed model is applied in the form of a latent class market segmentation model (to consider heterogeneity) on a Dutch dataset to understand the determinants of weekly time use and goods consumption behavior. Comparison of the values of time implied by the proposed model with those from simpler models proposed earlier in the literature suggest that ignoring either corner solutions and minimum consumptions or ignoring goods consumption in time use models can potentially lead to overestimation of the values of leisure and work times.

0-6850, There will be a significant increase in the number of renewable energy production facilities in Texas. The, construction of wind farms requires the transport of wind turbine components that create increased loads on, rural roads and bridges. These rural roads and bridges are typically not designed for such loads. This will result, in a greater burden on the transportation infrastructure in Texas., Given the upward trend in wind energy production, the Texas Department of Transportation (TxDOT) is, looking to plan for the impacts of future renewable energy projects on roads while facilitating the development, of new renewable projects in and around Texas. CTR created an operational planning tool that TxDOT can use, to propose route plans for wind turbine components passing along Texas routes and develop recommendations, for planning construction of new wind farms as well as maintenance strategies for the roads.

DTRT13-G-UTC58, This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an, associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal, distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is, estimated using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference, approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound, household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our, results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an, individual’s immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and, young individuals (18-34 years of age) in particular “warm up” to bicycling as more investment is made in, bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure, enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social, dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest, that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of, bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments, in bicycling infrastructure projects.