Real-World Performance of Longitudinal Barriers Struck by Large Trucks

Outside of relatively limited crash testing with large trucks, very little is known regarding the performance of traffic barriers subjected to real-world large truck impacts. The purpose of this study was to investigate real-world large truck impacts into traffic barriers to determine barrier crash involvement rates, the impact performance of barriers not specifically designed to redirect large trucks, and the real-world performance of large-truck-specific barriers. Data sources included the Fatality Analysis Reporting System (2000-2009), the General Estimates System (2000-2009) and 155 in-depth large truck-to-barrier crashes from the Large Truck Crash Causation Study. Large truck impacts with a longitudinal barrier were found to comprise 3 percent of all police-reported longitudinal barrier impacts and roughly the same proportion of barrier fatalities. Based on a logistic regression model predicting barrier penetration, large truck barrier penetration risk was found to increase by a factor of 6 for impacts with barriers designed primarily for passenger vehicles. Although large-truck-specific barriers were found to perform better than non-heavy vehicle specific barriers, the penetration rate of these barriers were found to be 17 percent. This penetration rate is especially a concern because the higher test level barriers are designed to protect other road users, not the occupants of the large truck. Surprisingly, barriers not specifically designed for large truck impacts were found to prevent large truck penetration approximately half of the time. This suggests that adding costlier higher test level barriers may not always be warranted, especially on roadways with lower truck volumes.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.