Enabling Data-Driven Transportation Safety Improvements in Rural Alaska

Safety improvements require funding. A clear need must be demonstrated to secure funding. For transportation safety, data, especially data about past crashes, is the usual method of demonstrating need. However, in rural locations, such data is often not available, or is not in a form amenable to use in funding applications. This research aids rural entities, often federally recognized tribes and small villages acquire data needed for funding applications. Two aspects of work product are the development of a traffic counting application for an iPad or similar device, and a review of the data requirements of the major transportation funding agencies. The traffic-counting app, UAF Traffic, demonstrated its ability to count traffic and turning movements for cars and trucks, as well as ATVs, snow machines, pedestrians, bicycles, and dog sleds. The review of the major agencies demonstrated that all the likely funders would accept qualitative data and Road Safety Audits. However, quantitative data, if it was available, was helpful

Scalable Spherical Harmonics Hierarchies

Scalable Spherical Harmonics Hierarchies (SSPHH) is a real-time rendering solution to the global illumination problem. Our novel method is a system of components that enables the computation of light probes and the conversion to spherical harmonics coefficients, which are used as anisotropic time-varying light sources we call Spherical Harmonics Lights (SPHLs). The SPHLs encode irradiance information that can be used for imagebased lighting. Our approach focuses on reconstructing scene lighting using diffuse illumination but is flexible to allow specular details. Furthermore, we consider the light transport from neighboring SPHLs by computing a transfer coefficient that estimates how much light from one probe is visible at another. SPHLs can be used for physically-based lighting using rendering methods similar to point lights and shadow maps. We created a reproducible testing methodology to compare our images with those of a commercial path tracer by automatically generating the appropriate scene information which gets used with absolute error metrics to determine remaining image defects. Our SSPHH method utilizes a scalable architecture to distribute the rendering of light probes between client and worker nodes using the ZeroMQ Majordomo protocol