ASU Scholarship Showcase

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.

Summer daytime cooling efficiency of various land cover is investigated for the urban core of Phoenix, Arizona, using the Local-Scale Urban Meteorological Parameterization Scheme (LUMPS). We examined the urban energy balance for 2 summer days in 2005 to analyze the daytime cooling-water use tradeoff and the timing of sensible heat reversal at night. The plausibility of the LUMPS model results was tested using remotely sensed surface temperatures from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and reference evapotranspiration values from a meteorological station. Cooling efficiency was derived from sensible and latent heat flux differences. The time when the sensible heat flux turns negative (sensible heat flux transition) was calculated from LUMPS simulated hourly fluxes. Results indicate that the time when the sensible heat flux changes direction at night is strongly influenced by the heat storage capacity of different land cover types and by the amount of vegetation. Higher heat storage delayed the transition up to 3 h in the study area, while vegetation expedited the sensible heat reversal by 2 h. Cooling efficiency index results suggest that overall, the Phoenix urban core is slightly more efficient at cooling than the desert, but efficiencies do not increase much with wet fractions higher than 20%. Industrial sites with high impervious surface cover and low wet fraction have negative cooling efficiencies. Findings indicate that drier neighborhoods with heterogeneous land uses are the most efficient landscapes in balancing cooling and water use in Phoenix. However, further factors such as energy use and human vulnerability to extreme heat have to be considered in the cooling-water use tradeoff, especially under the uncertainties of future climate change.