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The impact of transit investment on the access to economic opportunities and income inequality is an important question for researchers, transportation planners, and policymakers. This research conducts a comprehensive panel data analysis on the association between different types of transit investment (rail and non-rail) and various measures of income inequality for all U.S. Metropolitan Statistical Areas (MSAs) from 2011 to 2017. We find a significant effect of transit investment on reducing Gini coefficient and poverty rate in large MSAs with over a million population. The impacts seem to be driven mainly by adding new rail systems to traditional non-rail systems.

 

We present COBRA, a framework which enables a realistic hardware-guided methodology for evaluating compositions of hardware branch predictors. COBRA provides a common interface for developing RTL implementations of predictor subcomponents, as well as a predictor composer that automatically generates hardware predictor pipelines from sub-components based on a high-level topological model of a desired algorithm. We demonstrate how COBRA aids in the design and evaluation of diverse predictor architectures and how our hardware-centric approach captures concerns in predictor characterization that are not exposed in software-based algorithm development. Using COBRA, we generate three superscalar pipelined branch predictors with diverse architectures, synthesize them to run at 1 GHz on a commercial FinFET process, integrate them with the open-source BOOM out-of-order core, and evaluate their endto- end performance on workloads over trillions of cycles. The COBRA generator system has been open-sourced as part of the SonicBOOM out-of-order core.