modeling

This study models the active transportation and vehicular travel traveling modes in a multi-modal network problem taking into account TNC services, such as, e-hailing, e-pooling, and express pool. The objective is to analyze passengers' choices among active transportation, solo driving, and TNC services, considering various trade-offs related to health, monetary, time, and inconvenience costs, and their subsequent impacts on traffic network performance.

This white paper summarizes the development of MOVES-Matrix 3.0 based on EPA’s latest MOVES model known as MOVES3 (version 3.0.4).

This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD.

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.

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