modeling

Researchers configured MOVES to run on a distributed computing cluster, obtaining MOVES emission rate outputs for Atlanta for each vehicle class and model year at each operation, as a function of calendar year, local fuel, local I/M, meteorology, and other variables of interest.

This study approaches the problem of quantifying the network sensor errors as a supervised learning problem and leveraging deep neural networks to map observed traffic flow counts to the systematic errors in the sensors. The author aims at building a model that could reconstruct the erroneous flow irrespective of the level of random noise in the sensors, which is unknown in the real-world.

Extreme heat, intensified by urban heat islands and climate change, increasingly affects travelers, especially vulnerable populations such as older adults, people with disabilities, and those with chronic diseases. This dissertation develops a comprehensive system to evaluate cumulative heat exposure at the trip level, focusing on these vulnerable groups.

This report presents a review of the studies incorporating the Macroscopic Fundamental Diagram (MFD) dynamics for emissions estimation using various microscopic estimation frameworks.

In this study, a modal-based approach for estimating energy consumption and emissions of electric vehicles is proposed.