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). The research team updated the programs to account for changes in data structures and source sub-types and applied the same conceptual design used in MOVES-Matrix 2.0. The review of the MOVES3 and MOVES 2014b databases indicated a finer definition of the regions in terms of the unique combinations of fuel supply regions vs. Inspection/Maintenance (I/M) programs, with 40 fuel scenarios and 87 I/M scenarios in MOVES3 and 22 fuel scenarios and 84 I/M scenarios in MOVES 2014b. The increased number of fuel scenarios is due to the increased number of formulation regions and the one-to-many corresponding relationship between counties vs. fuel formulation regions by year. A total of 122 regions are defined in MOVES3 compared with 109 regions in MOVES 2014b, and the team anticipates at least 10% more running time to generate matrices for MOVES3, given the larger number of regions and the more complicated source type VSP/STP variables.

View the NCST Project Webpage

In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis.

View the NCST Project Webpage

The MOtor Vehicle Emission Simulator (MOVES) model was developed by the U.S. Environmental Protection Agency (USEPA) to estimate emissions from on-road and off-road vehicles in the United States. The MOVES model represents a significant improvement over the older MOBILE series of modes, primarily because emission rates are now truly modal in nature. Emission rates are now a function of power surrogates, which depend on speed and acceleration. Traffic simulation model outputs and smartphone GPS data can provide second-by-second vehicle activity data in time and space, including vehicle speed and acceleration. Coupling high-resolution vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the MOVES interface is complicated, and the structure of input variables and algorithms involved in running MOVES to assess operational improvements makes analyses cumbersome and time consuming. The MOVES interface also makes it difficult to assess complicated transportation networks and to undertake analyses of large-scale systems that are dynamic in nature.

The MOVES-Matrix system developed by the research team can be used to perform emissions modeling activities in a fraction of the time it takes to perform even one single individual MOVES run. The MOVES-Matrix approach involves running the MOVES model iteratively, across all potential input variable combinations, and using the resulting multidimensional array of pre-run MOVES outputs in emissions modeling. The research team configured MOVES to run on a distributed computing cluster, obtaining MOVES energy consumption and emission rate outputs for each vehicle class, model year, and operating condition, by calendar year, fuel composition (summer, winter, and transition fuels), local Inspection/Maintenance (I/M) program, meteorology, and other variables of interest. The team ran MOVES 146,853 times to generate the on-road emission rate matrices for Atlanta. More than 90 billion emission rates populate the primary output matrix, but implementation tools developed by the team generate matrix subsets for specific applications to speed up the analytical processes. In 2017-2018, the team developed MOVES-Matrix 2.0, which now integrates engine start, soak, evaporative, and truck hoteling emissions. The resulting emission rate matrices allow users to link emission rates to assess big data projects (such as regional emissions for emission inventory development) and to support near-real-time evaluations of changes in emissions for large, dynamic transportation systems. In the case study applications performed by the team, emission rate generation with MOVES-Matrix is 200-times faster than using the batch mode of MOVES graphic user interface in the same computer environment and the process predicts exactly the same emissions result.

View the NCST Project Webpage

Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in MARTA service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates. MARTA automated passenger count (APC) data were appended to the routes, and the energy use per passenger mile was compared across routes for the three periods. The results showed that the coupled effect of shift in transit frequency and decrease in ridership from 2019 to 2020 increased route-level energy use for more than 87% of the routes and per-passenger mile energy use for more than 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about the pre-pandemic level, but per-passenger energy use remained higher than pre-pandemic for more than 91% of the routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicated a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service.

View the NCST Project Webpage

Ongoing assessment of system performance monitoring is critical to successful and efficient transportation planning, ensuring that infrastructure investments provide a desired return on investment. As with any new transportation facility, it is important to understand how Express Lane facilities affect travel behavior, resulting on-road vehicle activity, and subsequent person-throughput (a function of vehicle occupancy) as part of the facility performance assessment. This report summarizes the vehicle and person throughput analysis for the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA, undertaken by the Georgia Institute of Technology research team for the State Road and Tollway Authority (SRTA). The research team tracked changes in observed vehicle throughput on four managed lane corridors and collected vehicle occupancy (persons per vehicle) data to assess changes in both vehicle throughput and person throughput associated with the opening of new Express Lane facilities. The team collected traffic volumes by video observation (GDOT’s Georgia NaviGAtor machine vision system and SRTA’s vehicle activity monitoring system). The team implemented a large-scale data collection effort for vehicle occupancy across all general purpose freeway lanes and from SRTA’s Express Lanes over a two-year period (before-and-after the opening of the Express Lanes). Between the baseline year (2018) and post-opening year (2019), the team observed a decrease in average vehicle occupancy (persons/vehicle), coupled with a significant increase in traffic volumes, especially on the NWC. The combined effect of increased traffic volumes and decreased occupancy still led to an overall increase in person throughput at all sites. Vehicle throughput on the I-85 corridor increased by about 5-7% and person throughput increased by 1-2% in the morning peak, and increased by around 10% for vehicles and 5% for persons in the evening peak. Vehicle throughput increased by more than 35% on I-575 in the AM and PM peaks, and by the same on I-75 in the AM peaks (only minor increases were noted in the PM peaks), likely due to the diversion of commute traffic from arterials onto the freeway corridor once the Express Lanes opened and congestion declined. Based upon vehicle throughput and occupancy distributions, the largest share of the increase in vehicle throughput in the peak periods came from an influx of single-occupant vehicle activity onto the corridor. Even though the number of carpools traversing the I-575 corridor increased slightly during the morning peak, the overall carpool mode share (percentage of carpools) decreased after the significantly greater numbers of single-occupant vehicles began using the corridor.

View the NCST Project Webpage