Crash modification factors for adaptive traffic signal control: An Empirical Bayes before-after study.

Adaptive traffic signal control (ATSC) is a novel traffic management system that is often deployed at high-volume intersections in order to mitigate traffic congestion and improve travel time reliability. While past studies have demonstrated its operational effectiveness, relatively few have focused on safety performance. Those that have tend to suffer from limitations including small sample sizes, insufficient study designs, or the lack of consideration of potential temporal and corridor effects after ATSC installation.

Evaluating the predictive power of an SPF for two-lane rural roads with random parameters on out-of-sample observations.

Negative binomial (NB) regression is among the most common statistical modeling methods used to model crash frequencies due to its simple functional form and ability to handle over-dispersion commonly found in crash data. However, a drawback of this approach is that regression parameters are assumed to be the same across observations, which could contribute to biased parameter estimates. To alleviate this concern, the random parameters negative binomial (RPNB) model was recently proposed, which allows regression parameters to differ across observations following some known distribution.

Application of a model-based recursive partitioning algorithm to predict crash frequency.

Count regression models have been applied widely in traffic safety research to estimate expected crash frequencies on road segments. Data mining algorithms, such as classification and regression trees, have recently been introduced into the field to overcome some of the assumptions associated with statistical models. However, these data-driven algorithms usually provide non-parametric output, making it difficult to draw statistical inference or to evaluate how independent variables are associated with expected crash frequencies.

Modeling and simulation of a transcutaneous energy transmission system used in artificial organ implants.

We present a mathematical model to simulate transcutaneous energy transmission systems. Treating such systems as resonant power electronic converters, we develop the equivalent circuit equations, for which the circuit variables are then expanded as Fourier series and a multi-frequency averaging method was applied. Keeping terms up to first-order, the analysis produces a dynamic and harmonic model describing these energy transmission systems.