Description

Traditional MOVES modeling of complicated and dynamic networks is time consuming and can require the generation of hundreds of link emission rates, which often means that users generate lookup tables to support modeling. MOVES-Matrix builds on the look-up concept by pre-running MOVES for all combinations of input data, compiling energy use and emission rates into a 120 Gb multi-dimensional matrix (five billion on-road energy use and emission rates) for each modeling region. Python scripts are then used to automatically assemble on-road fleet emission rates for any environmental scenario, specified fleet, and on-road operating conditions. To date, the Georgia Tech modeling team has used local supercomputing clusters to perform more than one million MOVES model runs and have populated MOVES-Matrix for more than 35 regions in the U.S. The resulting matrices provides tremendous flexibility and can be used with regional travel demand models, traffic simulation models, project-level corridor monitoring data, microscale pollutant dispersion models, and other applications at multiple spatial and temporal scales. The resulting matrices are large and complex, but the use of Python scripts makes applications run smoothly. The tools also provide exactly the same results as using MOVES directly. This presentation will provide an overview of the MOVES-Matrix modeling tools and demonstrate some of the integrated applications that have been published to date.