Electric Vehicles (EVs) significantly reduce energy consumption and emissions from on-road operations and help create more sustainable transportation environment by reducing emissions from the entire well-to wheel energy cycle. Differences between hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicles (BEV) users is an important element in understanding potential impacts on travel demand and vehicle adoption, the fact that these vehicles may be adopted into households that undertake very different vehicle activities and energy usage patterns has not been a primary focus in the literature. This study differentiates between HEV, PHEV, and BEV users across three factors: owner household socio-demographic attributes, household daily travel patterns, and household energy usage profiles. The analyses examine factors that appear to influence users’ preferences towards specific EV types and how the selection of different EV types potentially relates to household socio demographics and daily travel patterns. The 2019 Puget Sound Regional Council travel survey data set serves as the main analytical dataset. Influential factors identified as significant through statistical approaches are employed as variables for developing a two-phase choice model for determining potential EV-purchasing households and their choice of specific EV type. As EVs continue to capture increasing market share over time, these research findings and the resulting vehicle type choice model are expected to significantly improve future travel demand model development, allowing activity-based travel demand models to assign specific vehicles to specific households and then to individual trips in planning scenario analysis.

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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.

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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.

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Since heavy-duty truck operations can significantly affect traffic congestion, especially on road grade, the creation of exclusive lanes for trucks has been viewed as a potential alternative to reduce congestion delay, fuel consumption, and emissions. However, few studies have rigorously evaluated the effectiveness of truck-only lanes in achieving these benefits. This study demonstrates a model framework that combines a microscopic traffic simulation with emissions and microscale dispersion models to quantify the potential impacts of truck-only lanes on fuel consumption, emissions, and near-road pollutant concentrations. As a case study, the framework was used to evaluate a proposed $2 billion project to construct 40-miles of truck-only lanes on Interstate 75 (I-75) between Atlanta and Macon, Georgia (USA).

The findings of this study suggest that truck-only lanes could significantly improve the traffic flow, and reduce energy, emissions, and pollutant concentrations. The research team expects that the extensive simulation results of this study help to understand the performance of truck-only lanes on a large-scale network with a heavy mixture of truck and general purpose lane traffic. The methodology and framework developed in this study can be effectively and efficiently applied to a wide variety of scenarios to evaluate the environmental impacts of other transportation projects under various conditions.

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The cost of fuel represents a major portion of the costs of operating on-road heavy-duty vehicles (HDV). Over the next couple of decades, the total energy demand from the HDV sector will likely increase due to forecasted growth in freight demand in many global markets, including the United States, and much of this energy will continue to be provided by fossil fuels. Therefore, carbon dioxide emissions from the HDV sector are also expected to increase in the absence of effective mitigating measures to reduce the sectors reliance on fossil fuels. Along with other fuel-saving technologies, the United States Environmental Protection Agency identified the use of Low Rolling Resistance (LRR) tires as an effective method of reducing fuel consumption. It is estimated that LRR tires can improve fuel economy in HDV by about 10 percent. However, adoption of LRR faces many barriers and the most fundamental of these barriers relate to potential performance uncertainties under real-world operating conditions. Previous published decision support tools developed to help fleet operators and other stakeholders estimate the fuel-savings from LRR tires have been found to have limited accuracy due to inherent transient speed profiles in real-world operating cycles.

In this study, we develop a tool to predict the fleet-wide fuel-saving benefits from low rolling resistance tires. Unlike previous studies, the developed tool is applicable to both stabilized speed operations and transient speed operations. The tool is based on empirical models that estimate the fuel consumption contribution from tires as a function of vehicle payload, aerodynamic drag, road grade, duration of acceleration, duration of deceleration and, and road facility type (freeway, major arterial, and minor arterial/local road). We limited the scope of the developed tool to tractor-trailers in the U.S. heavy-duty vehicle market, because the United States has the second largest HDV market in the world and tractor-trailers account for the largest share of the market. The tool was developed with data generated by simulating real-world heavy-duty vehicle operating cycles with Autonomie®, the state-of-the-art model for automotive control-system design, and simulating vehicle energy consumption and performance. Autonomie® is a preferred vehicle simulation tool of the United States Department of Energy.

The primary purpose of the Tool to Predict Fleet-Wide Heavy-Duty Vehicle Fuel-Saving from Low Rolling Resistance Tires is to assist fleet operators, regulatory agencies, and policy analysts in assessing the fuel consumption savings from low rolling resistance tires. To facilitate ease-of-use by stakeholders, the statistical empirical models are embedded in a Microsoft Excel® spreadsheet. Fleet managers can customize the tool to their specific fleet and the tool is designed to inform fleet operators about the benefits and costs of making low rolling resistance tire investments. In addition to fuel consumption estimates, the spreadsheet tool further estimates related emission reductions. In the future, this tool can be extended to other vehicle segments. The spreadsheet algorithms can also be developed into a web-based computer program in the future to facilitate online use of the tool.

The HDV Low Rolling Resistance Tire Fuel and Emission Reduction Calculator is available to download as a spreadsheet tool here: http://transportation.ce.gatech.edu/node/95

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The goal of this research is to develop a tool that can help students and the public better understand the total cost of owning and operating a personal vehicle over the lifetime of that vehicle, from vehicle purchase to vehicle disposal.  Lifetime vehicle operating cost consists of the vehicle purchase cost, as well as costs that are less obvious to the user (e.g., financing, fuel, maintenance, insurance, etc.).  Underestimating the hidden costs embedded in lifetime vehicle operating cost can lead people to choose vehicles that are unaffordable for their financial situations.  Hence, educating transportation planners, students, and the public about lifetime vehicle operating costs is important.

To provide a more personalized calculation approach, the National Center for Sustainable Transportation (NCST) research team developed the Lifetime Vehicle Ownership and Operating Cost Calculator.  The NCST website provides users with data entry flexibility and a clear, intuitive, and interactive user-interface through which users can better understand their vehicle ownership costs.  Vehicle ownership costs are calculated using lookup data and user input for such elements as: purchase price, down payment, interest rates, loan term, annual insurance, maintenance, tire, smog check, parking, tolls, etc.  All lookup data can be modified as desired by the user.

The NCST website is designed to provide students and the public with a tool that can be used in research and also at any educational level.  Users can calculate the cent/mile and total cost of vehicle ownership over the life of the vehicle, with cost estimates for each component specifically identified so that users can weigh the impact of each cost element in their vehicle purchase and use decisions.  From an educational perspective, this tool can be used as an instructional aid to specific courses.  For example, the tool has been used in the urban transportation planning course at Georgia Tech, where the students are asked to compare different vehicle choices based on a real-life case study.  In addition, the NCST team plans to introduce this tool to high school consumer economics classes to help students make reasonable vehicle purchase decisions and to assess vehicle operating costs over entire vehicle life cycles.

The Lifetime Vehicle Ownership and Operating Cost Calculator is available at http://costcalculator.ce.gatech.edu/.

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The U.S. Environmental Protection Agency identified the use of low rolling resistance (LRR) tires as an effective method of reducing vehicle fuel consumption, especially from heavyduty vehicles (HDV). LRR tires are important to HDV operations because fuel accounts for about 25% of operating costs, and improving fuel economy also reduces emissions of both greenhouse gases and oxides of nitrogen, a precursor to the formation of ozone, which is harmful to humans, plants, and animals. However, their adoption rate has been slow primarily due to performance uncertainties under real-world operating conditions. Previous mathematical models developed to help fleet operators estimate the impact of LRR tires on their operations have suffered from poor accuracy because they do not account for variable speed profiles in realworld HDV operations. Georgia Tech researchers have developed a new tool for fleet managers that better predicts the benefits of LRR tires under real-world conditions.

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The Motor Vehicle Emission Simulator (MOVES) model was developed by the US 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 much more modal in nature, representing emissions as 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 data. 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 compared to running MOVES. The approach involves running the MOVES model iteratively, across all potential input variable combination, and using the resulting multidimensional array of pre-run MOVES outputs in emissions modeling.

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The National Center for Sustainable Transportation (NCST) seeks to ensure that transportation professionals have the necessary knowledge and skills to design, operate, and maintain sustainable transportation systems. This report provides a structured review of existing sustainable transportation courses to serve as foundation for the development of NCST model curriculum and to support course development elsewhere. This report provides an inventory of transportation-related courses and course concepts. The report also surveys models and modeling support tools, current research topics, and industry job descriptions to identify room for growth in sustainable transportation curricula.

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Eco-driving has significant potential to reduce fuel consumption and emissions from transit operations. Analyses were conducted of 68 thousand miles of real-world operations data from 26 buses, collected from local transit service provided by the Metropolitan Atlanta Rapid Transit Authority (MARTA), and express bus service provided by the Georgia Regional Transportation Authority (GRTA). The analysis utilized second-by-second operations data collected via global positioning system (GPS) devices from buses operated by these transit agencies. The researchers simulated the implementation of transit eco-driving strategies, based on the modal emissions modeling framework employed by the Motor Vehicle Emission Simulator (MOVES) designed to reduce engine load and emissions. This algorithm seeks to minimize fuel consumption by limiting instantaneous vehicle specific power (VSP), while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles.The savings from eco-driving were also compared against expected fuel and emissions reductions via conversion of the transit fleets to compressed natural gas (CNG), which is another popular fuel conservation strategy. The transit eco-driving strategy showed a 5% reduction in fuel consumption and fuel cycle greenhouse gas (GHG) emissions for MARTA’s 508-bus fleet (~35% diesel/65% CNG), and a 7% reduction in fuel consumption for GRTA’s 166-bus diesel fleet. The fuel savings translate to about 300,000 gallons of diesel fuel equivalent per year for MARTA and 55,000 gallons of diesel per year for GRTA. Eco-driving was also shown to reduce fuel use and emissions for CNG fleets. Eco-driving training can readily be implemented if speed/acceleration activity is monitored. Because eco-driving does not require significant capital investment it is a potentially very cost-effective strategy for local and express bus transit operations.

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