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Current frameworks for analyzing emissions performance of public transportation systems use top-down approaches that can often provide useful information at the network level but can be uninformative at the project level at which the influence of route and vehicle characteristics can significantly impact emission profiles of candidate transit options. This paper describes an alternative bottom-up framework that uses second-by-second travel activity data to estimate total power consumption and related emissions for propulsion purposes with application to electric rail transit systems. The model was developed and calibrated with data from Portland, Oregon, and was supplemented with activity data from Chicago, Illinois. The results showed a predicted 1% to 8% difference in expected power consumption relative to estimates derived from the national transit database. In addition, the results highlighted how the speed profile, configuration of the train in number of cars, and mix of power generation sources could significantly vary emissions performance across different service routes. The developed framework can serve as an important tool for a transit planner or policy maker to evaluate the emissions performance of electric rail transit options. This framework has the advantage of relevance at both the network and project levels. At the project level, users can easily perform detailed sensitivity analysis on aspects of transit services such as vehicle and fuel technologies, passenger loading profiles, train size, and track profile. This framework gives transportation planners a flexible and efficient tool for emissions performance analysis.

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U.S. roundabout growth has been significant in recent years and many published studies have documented significant safety benefits of roundabouts. However, the safety benefits for a roundabout may vary from region to region depending on many local factors. Therefore, transportation agencies can make more informed implementation decisions with local safety evaluations rather than published national findings. However, roundabouts are relatively new in the United States and most departments of transportation, including Georgia, are often hindered by the data availability requirements of the state-of-the-art empirical Bayes analysis evaluation procedure. This current study provides a safety evaluation of 23 Georgia roundabouts. It adopts a time-dependent form of the Highway Safety Manual predictive (empirical Bayes) method to estimate potential crash reductions across all crashes and all injury/fatal crashes. The method extends the empirical Bayes procedure towards a full Bayesian analysis. The findings indicate a 37–48% reduction in average crash frequency for all crashes and a 51–60% reduction in average crash frequency for injury/fatal crashes at four-leg roundabouts that were converted from stop-controlled and conventional intersections. In addition, when analyzed as a group, three-leg and four-leg roundabouts converted from stop-controlled and conventional intersections collectively experienced 56% reduction in average crash frequency for all crashes and 69% reduction in injury/fatal crashes. The study did not consider five-leg roundabouts because of small sample size and concerns about the form of the safety performance function. The adopted methodology offers departments of transportation with data availability challenges an alternative evaluation framework that retains the positive attributes of empirical Bayes analysis.