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Purpose – Measures aimed at reducing intersection crashes have high potential to be cost effective since intersections constitute only a small part of the overall highway system but intersection-related crashes constitute more than 50% of all crashes in urban areas and over 30% in rural areas. Roundabouts are a proven safety countermeasure, but several issues that significantly affect both crash frequency and severity have been observed at both existing and new roundabouts. This chapter aims to provide guidance on roundabout selection and design criteria.

Methodology – The chapter first describes the most relevant criteria to be considered for choosing a roundabout. Then, after the explanation of the roundabout design process and a clear description of the roundabout classification, the chapter provides recommendations for all the steps of the geometric design, highlighting the main design features that contribute to the best safety performances, including speed control and sight distance checks. Finally, the chapter explains traffic control devices and facilities for pedestrians and cyclists.

Findings – Roundabout design needs to balance opposing demands and it is important to adopt a performance-based design approach within an iterative process. The most important performance check is the analysis of vehicle speeds through the roundabout, since achieving appropriate vehicular speeds has a very positive safety effect.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).