ASU Scholarship Showcase

The current study examined heterogeneity in emerging adult children's routine and self-disclosure to parents using mixture modeling and explored predictors and outcomes associated with the patterns of disclosure. Participants consisted of 449 emerging adults (49% male, 68% European American, 65% college students, 33% single-parent families) who completed questionnaires every year across three waves (Mage at Time 1 = 18.4 years). Latent profile analyses suggested that large groups of emerging adults reported moderate levels of routine disclosure and low levels of self-disclosure to both mothers (79%) and fathers (36%), while other groups (20%) reported high levels of routine and self-disclosure to both parents. Profile membership was associated with predictors (parental autonomy granting, self-disclosure to friend, gender, family structure, college attendance) at Time 1 and outcomes (delinquency, depression, and prosocial behavior) at Time 3. Implications regarding the continued parent-child relationship and disclosure to parents in the third decade of life are discussed.

The following literature review talks about the driving simulation platforms commercially available for automated vehicle development. It is also a comparison of the simulation packages, their advantages and drawbacks, and an insight into what is missing in the simulators of today. Automated vehicle safety and reliability are the important requirements when developing automated vehicles. These requirements are guaranteed by extensive functional and performance tests. Conducting these tests on real vehicles is extremely expensive and time consuming, and thus it is necessary to develop a simulation platform to perform these tasks. In most cases, it is difficult for system or algorithm developers in the testing process to evaluate the massive design space. To test any algorithm change, developers need to test a functional module alone, and later setting up a whole physical testing environment that consists of several other modules, leading to enormous testing costs. Fortunately, many of the testing tasks can be accomplished by utilizing simulator. The key to the success of a simulation is how accurately the simulator can simulate the physical reality.

There is a need for indicators of transportation-land use system quality that are understandable to a wide range of stakeholders, and which can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing down calculations. We present new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning, while being rigorous enough for final analyses. Our approach is statistical, characterizing the uncertainty and variability in accessibility metrics due to differences in departure time and headway-based scenario specification. The analysis is carried out on a detailed multi-modal network model including both public transportation and streets. Land use data are represented at high resolution. These methods have been implemented as open-source software running on commodity cloud infrastructure. Networks are constructed from standard open data sources, and scenarios are built in a map-based web interface. We conclude with a case study, describing how these methods were applied in a long-term transportation planning process for metropolitan Amsterdam.