emissions

This report describes the development and use of a U.S. energy system optimization model (US-TIMES) in order to analyze the reductions in greenhouse gas emissions that can come about through policy targets.

In this study, both stationary and comprehensive mobile monitoring platforms were developed to measure PM concentration and traffic conditions on some major highways in Southern California. The main objective is to develop a set of alternative metrics that measure particle concentrations as well as particle size.

This study used 79,328 receptor-link pairs randomly selected from the Atlanta Metropolitan area to train and test a machine learning random forest classification algorithm.

The research objectives of this dissertation are to develop a modeling framework within an advanced activity-based travel demand model that can be used to assess the distribution of transportation cost and benefits across communities of interest and implement a data structure and variable tracking system that can be implemented within and across various modeling tools to ensure that data needed for equity assessment transfer between models, and that outputs from combined models can be used to assess equity impacts.

This work proposes a strategic receptor placement method, called dynamic-receptor-grid model (DGRM), that identifies the optimal receptor positions across a region, while preserving the high-resolution pollutant concentration profiles predicted by dense receptor placement.

The authors simulated the implementation of transit eco-driving strategies using an innovative, streamlined algorithm designed to minimize fuel consumption and discuss their findings in this publication.