Improved Energy and Emissions Modeling for Project Evaluation (MOVES-Matrix)

Principal Investigator: Randall Guensler | Georgia Institute of Technology
Co-Principal Investigator(s): Yanzhi Xu | Georgia Institute of Technology; Michael Rodgers | Georgia Institute of Technology; Michael Hunter | Georgia Institute of Technology

State and local governments are using the sophisticated MOVES emission rate model to estimate regional and project-level energy consumption and emissions for air quality analyses.  However, MOVES run for regional modeling can take days to process and it is easy to make model input errors, due to the complex nature of the model.  The research team developed the MOVES-Matrix modeling approach to simplify the modeling process and increase data processing speeds by more than two orders of magnitude.  MOVES-Matrix runs the MOVES model across all possible model input data permutations and creates a multi-dimensional emission rate lookup array that can be used in any emissions analysis.

By pre-processing the MOVES model millions of times for all possible scenarios in which energy consumption and emissions rates will be used, modelers no longer need to be experts in the operation of the MOVES model.  Modelers can focus on understanding and properly specifying fleet composition, environmental conditions, traffic operations, and other data that affect emission rates.  The MOVES-Matrix system automatically provides the applicable emission rates for use in the regional and project-level analysis.  Users specify vehicle class and technology distributions, environmental parameters, and on-road operating conditions, and the automated procedures generate the applicable energy use and emission rates from the Matrix.  The team has integrated MOVES-Matrix with the Atlanta Regional Commission’s regional travel demand model, the VISSIM simulation model, dynamic traffic assignment, the Georgia Tech transit simulation model, and monitored second-by-second individual vehicle data.  The system has also been demonstrated in microscale air quality impact assessment using the AERMOD and CALINE4 models.

Status: Completed
Funding: $92,292.15
Sponsors: US DOT

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