An Integrated Tool for Modeling the Impact of Alternative Fueled Vehicles on Traffic Emissions: A Case Study of Greenville, South Carolina

Transcription

1 An Integrated Tool for Modeling the Impact of Alternative Fueled Vehicles on Traffic Emissions: A Case Study of Greenville, South Carolina Yuanchang Xie*, Ph.D., P.E. Assistant Professor Civil and Mechanical Engineering Technology South Carolina State University Orangeburg, SC yxie@scsu.edu Mashrur (Ronnie) Chowdhury, Ph.D., P.E., F.ASCE IDEAS Associate Professor Department of Civil Engineering Clemson University mac@clemson.edu Parth Bhavsar, Ph.D. Student Department of Civil Engineering Clemson University parthb@clemson.edu Yan Zhou, Ph.D. Center for Transportation Research Argonne National Laboratory Argonne, IL yzhou@anl.gov Revised paper submitted for presentation at the Transportation Research Board 90 th Annual Meeting November 15, 2010 Word count: 5, Tables + 2 Figures = 7,433 Words * Corresponding author

2 Xie et al An Integrated Tool for Modeling the Impact of Alternative Fueled Vehicles on Traffic Emissions: A Case Study of Greenville, South Carolina Yuanchang Xie, Mashrur (Ronnie) Chowdhury, Parth Bhavsar, and Yan Zhou ABSTRACT The United States recently has been investing heavily in renewable energy technologies to reduce dependence on foreign oil and cut greenhouse gas emissions. The impacts of using alternative transportation fuels have also attracted significant attention. This research focuses on evaluating the environmental impacts of three alternative transportation fuels: electricity, ethanol and compressed natural gas. The goal is to estimate the impacts of alternative fueled vehicles at a project level in terms of daily fuel savings and emission reduction. In this research, a review of existing studies on alternative transportation fuels and their environmental impacts is performed. In addition, a case study is conducted using the Environmental Protection Agency s (EPA) latest vehicle emission model, MOVES, and the PARAMICS microscopic traffic simulation tool to analyze the emission impacts of alternative transportation fuels for a road network in Greenville, South Carolina. For each alternative transportation fuel considered in this study, its emission and fuel consumption impacts are evaluated based on different market shares. The results show significant positive environmental impacts from using the three alternative transportation fuels. The findings of this paper can be useful to other transportation researchers and practitioners in conducting environmental impacts studies using MOVES. KEY WORDS Alternative fueled vehicles, MOVES, PARAMICS, Simulation, Emissions, Greenhouse gas, Biofuel

3 Xie et al INTRODUCTION During the last decade, Americans have become increasingly concerned about the sustainability of future mobility due to dependence on foreign oil, environmental issues, and a number of other reasons. Indeed, with unpredictability in gasoline prices and the huge amount of greenhouse gas emissions from the transportation sector, the concern has never been even more apparent. Therefore, both the US and the world must create technologies that can reduce the burden on congestion and meet the energy demand supporting national and global economies, enhance national energy security, and improve environmental quality. During the last few years, several alternatives to petroleum for automobile use have been proposed. These include hydrogen/fuel cell vehicles, ethanol, compress natural gas, liquefied natural gas, liquefied petroleum gas and electricity. These alternative fuels provide an important opportunity to contribute to a sustainable transportation system in terms of improved economy, conserved energy and reduced pollution. There is no doubt that shifting from regular gasoline and diesel to alternative transportation fuels can help reduce air pollution and improve quality of life. However, it is not an easy task to quantify such impacts. The objective of this research is to evaluate the environmental impacts of alternative transportation fuels. Specifically, we focus on estimating how alternative fueled vehicles can make substantial impacts at a project level in terms of daily fuel savings and emission reduction. The Environmental Protection Agency s (EPA) latest vehicle emission model, MOVES, and the PARAMICS microscopic traffic simulation tool are utilized to analyze such impacts. The modeling results can be incorporated into other models for further analysis (e.g., economic impact analysis) and can also be used to help decision makers develop better energy policies. As the US and the entire world is moving towards long-term sustainable development, there will be more and more research efforts undertaken to investigate the impacts of alternative energy on the transportation industry. The analysis presented in this paper can be useful to other researchers in understanding how to utilize MOVES to quantify the environmental impacts of alternative transportation fuels. RELATED WORK The current EPAct has recognized several fuels that are considered alternative transportation fuels (Yacobucci, 2006). These alternative fuels, which include liquefied petroleum gas (LPG), natural gas, biodiesel, ethanol, methanol, hydrogen, and electricity, have been successfully commercialized in both private and public sectors. Although other alternative fuels are not widely used in the transportation industry, economic effects, government tax policies and regulatory mandates will continue to encourage the increased use of them. Given the popularity of the alternative transportation fuels, a few studies have been conducted to investigate their environmental impacts. Sponsored by the U.S. Department of Energy, Argonne National Laboratory has developed a GREET model, which is a full life cycle model for estimating the total energy consumption and emissions of new vehicle/engine technologies and alternative transportation fuels. GREET can be used to analyze the emissions generated during production, distribution, and use of alternative transportation fuels. In addition to its full-cycle emission analysis capability, GREET also has a vehicle model that estimates total energy consumption. GREET is developed based on the Microsoft Excel platform and has a user-friendly interface. It can model emissions including GHGs (e.g., CO 2, methane, and nitrous oxide), NOx, HC, CO, sulfur dioxide (SO 2 ), and particulate matter (PM). GREET can

4 Xie et al also model many types of engine/vehicle technologies including conventional spark-ignition engines, direct injection engines, fuel-cell vehicles, plug-in hybrid electric vehicles, and batterypowered electric vehicles (Winebrake et al., 2000). POLCAGE is a model developed for the comparative life-cycle assessment (LCA) of different fuel options for the Philippine automotive transport sector. Tan et al. (2004) develop this tool with the capability of ranking alternative transportation fuels using a comprehensive life-cycle assessment strategy. A major advantage of POLCAGE is the availability of a combination of inventory impact assessments and the environmental design of industrial products (EDIP) method. The model thus provides users with two levels of information: the magnitude of environmental effects and the associated confidence levels of these estimates. Several other studies have also been conducted to investigate the environmental impacts of alternative fuels. Chang and Rudy (1990) compare the Ozone-forming potential of emissions from various alternative fueled vehicles and gasoline-fueled vehicles. Wyman (1994) analyzes the impact of biofuels on carbon dioxide accumulation. Kazimi (1997) investigates the total emissions in the Los Angeles area using a dynamic micro-simulation model. He employs discrete choice models to estimate the market shares of three types of alternative fueled vehicles: electric, compressed natural gas (CNG), and methanol vehicles under different price options. Based on their emission rates and estimated market shares, the author thus is able to predict the reduction in traffic emissions and the associated health benefits. In this research, Kazimi uses vehicle emission rates from the California Air Resources Board s EMFAC Model and EPA s MOBILE model. Hackney and Neufville (2001) develop a life cycle model and a spreadsheet tool to evaluate the emissions, costs, and fuel efficiency trade-offs of alternative fueled vehicles. The life cycle model has the advantages of including the full life cycle of fuels and vehicles. It can avoid the distortions during the analysis generated by taxes or other incentives. Different from the previous studies, this research focuses on using the latest MOVES tool developed by the US Environmental Protection Agency and a microscopic traffic simulation model to analyze and quantify the environmental impacts of alternative transportation fuels. Unlike the life cycle model that considers the full life cycle of fuels and vehicles (Hackney and Neufville, 2001), this study is conducted mainly from a transportation perspective and is concentrated on the impacts of using alternative transportation fuels. Given the detailed output data from MOVES and the microscopic traffic simulation model, the method developed in this research is expected to substantially improve the depth and breadth of analyzing the environmental impacts of using alternative transportation fuels. The findings can help planners and researchers better understand the different impacts of various alternative fuels. In addition, since MOVES is relatively new and has not yet been widely used in the transportation industry, this study can provide useful guidance on its application. MODELING TOOLS PARAMICS Microscopic Traffic Simulation PARAMICS is a microscopic traffic simulation program and can simulate the second-by-second movements of pedestrians and various types of motor vehicles. It consists of Analyser, Converter, Designer, Estimator, Modeller, Processor, etc. Modeller is for network editing and simulation. Analyser is then used to analyze the simulation results generated by the Modeller and to create different kinds of statistics and reports. Each of the remaining tools has its specific purpose. The Modeller tool has a number of plug-ins. One of them is Monitor, which is

5 Xie et al designed for estimating traffic air emissions (Quadstone, 2009). The Monitor plug-in is developed based on extensive pollution test results. The collected pollution data are used to cross reference the simulated vehicle speeds and accelerations to various types of pollution emissions for different engine technologies. The Monitor tool also makes it possible for users to incorporate their own emission rates and to improve the pollution estimates. MOVES Motor Vehicle Emissions Simulator (MOVES) is developed by the US Environmental Protection Agency (EPA) to estimate air pollution, brake, and tire wear emissions from various types of onroad vehicles. Given different input data, such as road network, vehicle compositions, time, place, and fuel types, MOVES can estimate the corresponding emissions and fuel consumption. An earlier version of MOVES is MOBILE6.2, which is also developed by EPA. The latest version of MOVES is MOVES2010, which was released in April 2010 (US EPA, 2010a). MOVES2010 incorporates many up-to-date emission test results and can model several new fuel types and engine technologies, including biofuels and electric vehicles. Thus, MOVES2010 is adopted for this research. For ease of description, MOVES is used hereinafter to refer to MOVES2010. In addition to modeling traffic air pollutants, MOVES can be used to estimate greenhouse gas emissions and energy consumption at national, county, and project levels. Project level analysis is typically for intersections, arterials, or small road networks. MOVES has a built-in database that provides basic input data to run models at the national level. Although these data may also be used for county and project level analyses, it is recommended that local data be used in these cases. MOVES is designed in a very open and flexible manner. For county or project level applications, users can easily incorporate local data to replace the default values in the database and to improve modeling result accuracy. The default data in the built-in database are collected from various sources, including EPA, Census Bureau, and Federal Highway Administration, and are being updated constantly to ensure accurate modeling (US EPA, 2010b). MOVES is used in this research as a major tool to assess the traffic emission impacts of alternative transportation fuels. To apply MOVES, one can choose either the inventory method or the emission rate lookup table method. The inventory method takes data from transportation planning models, traffic simulation models, and other sources to generate the required input for MOVES. These data are prepared in certain formats stipulated by MOVES and are used to replace the default values in the built-in database. MOVES provides several data importers and managers that can facilitate the import of the required input data. Based on these data and the models built into it, MOVES then generates traffic emission estimates for the study area. Another approach of applying MOVES is the emission rate lookup table method. In this case, MOVES is used mainly to generate emission rates for different types of vehicles under various operating conditions. These emission rates are combined with the outputs from transportation planning or traffic simulation models to estimate total traffic emissions. In the case of PARAMCIS, the emission rate outputs from MOVES can be incorporated into the Monitor plug-in. The generated emission rates are stored in a MOVES output database and can be used repeatedly. Depending on the size of the study area and the time span selected, a single run of MOVES may take considerable amount of time. If this is the case, the emission rate lookup table method can offer great benefits in terms of computation time saving. Given the limited amount of time, only the MOVES inventory method is considered in this study.

6 Xie et al Integration of Modeling Tools PARAMICS was integrated with MOVES in this study for evaluating the impacts of alternative transportation fuels. Figure 1 shows how these two tools were integrated for this research. The study network was first modeledd in the microsimulation software PARAMICS. The network and the simulation model were then calibrated and validatedd to better represent real world scenarios. As shown in Figure 1, the outputs from the PARAMICS simulation were summarized and used as input to the emission simulation software MOVES. PARAMICS MOVES Classification Data Vehicle type (Source type) configuration from EPA Create Network Simulate Network Output Link volume and speed data Link sourcetype distribution data Outputt from simulation Default MOVES data Site specific Data Create runspec Simulate Emission Model Rate per VMT Figure 1. Integration of PARAMICS and MOVES DATA The following two sections present the data collection and network coding efforts that supported the emission impact analysis of alternative fueled vehicles through an integrationn of MOVES and PARAMICS. Data Collection The following data were collected as the input to the PARAMICS simulation: o Geometric Data: Shape files from the online GIS database of the South Carolina Department of Natural Resources (SCDNR) were used to build the study network. We also collected geometric data in the field to incorporate the latest modifications in the infrastructure. In addition, design and planning sheets were obtained from South Carolina Department of Transportation to verify lane configurations. o Traffic Volume: The origin Destination (OD) matrix method in PARAMICS was used to generate traffic on the network. Traffic data were collected at the end points of the network (i.e., gateways of the network). These counts represent vehicular traffic entering or leaving the network. A Matlab code with logical constraints was developed to repopulatee the OD matrix to replicate the real world. This matrix was then calibrated after every simulation run to achievee the desiredd accuracy.

7 Xie et al o In addition, it is very important to make sure the vehicle types used in PARAMICS are consistent with those defined in MOVES (i.e., source types). The default values of vehicle type characteristics in PARAMICS were changed to match the source type in MOVES. The percentage of each vehicle type in the simulation was changed according to the classification data collected in field. We double checked all vehicle types used in PARAMICS to ensure the consistency. Once the results from the PARAMICS simulation were available, they were summarized and used as input to the MOVES model. In this research, the project domain manager in MOVES was used to import the PARAMICS simulation results into MOVES. The project domain manager required project specific data in either Microsoft SQL Database format or Microsoft Excel Format. These data were then populated with the following information. o Meteorology: Historical temperature and humidity information was collected from the National Climatic Data Center website created by the Unites States Department of Commerce. o Fuel Formulation and Fuel Supply: Default MOVES fuel formulation data were used as the basis to model different fuel types. To model the environmental impacts of different alternative transportation fuels, several assumptions were made regarding their market shares and the percentages of vehicles using them. The default MOVES fuel formulation and fuel supply data were modified based on the assumptions made. o Age Distribution: Default vehicle age distribution provided in the MOVES database was used. o Links and Link Source Type: Analyser module of PARAMICS and Microsoft (MS) Access were utilized to generate the desire input for MOVES. The PARAMICS Modeller module can create a snap shot of data points at a given time interval that can be decoded in the Analyser module. The current version of the Analyser module does not provide link length data along with average speeds of links. Fortunately, the PARAMICS Modeller can export network links as well as node information in a comma separated (.CSV) file. This file along with the Analyser output was then imported into the MS Access database. The final input tables consisting of Link and Link Source Type Hour were created in MS Access by establishing a relationship between the.csv file and the Analyser output. Network Coding A segment of freeway in Greenville, South Carolina was selected for the case study. This freeway segment was coded using PARAMICS Modeller tool to create the simulation network for this research. The shape files from SCDNR were converted into the PARAMICS road network by the Shape to PARAMICS (S2P) tool developed by the California Department of Transportation. After converting roadway characteristics into PARAMICS format, the next step was to check each intersection and interchange of the PARAMICS network against the existing design. Scaled satellite images were inserted as a background to verify the design. Since the satellite images do not always represent the current data, we also cross checked the geometric design with field data. Planning sheets were also utilized where needed to check lane

8 Xie et al configurations and other information. Figure 2 shows the entire Greenville network. All interchanges and intersections were updatedd to replicatee existing infrastructure. Because of the computation time limit, only part of the network was considered in this study, which is shown in the two highlighted red circles in Figure Figure 2. PARAMICS Modeller Network The PARAMICS Analyser tool was used to extract the desired output neededd for MOVES. PARAMICS Modeller can create an outpu of data snap shots of the network at desired time intervals. These snap shots can be configured to outpu different types of data, for example, vehicle count, vehicle types, and average speed for each link. As shown in Figure 2, sections of Interstatee 85 with three interchanges from Exit 46 to Exit 48 B were selected as a reduced network to be modeled in MOVES. The entire Greenville network as shown in Figure 2 was simulated. PARAMICS Analyser was then used to output the data for the reduced network only. Microsoft Access was used to create the final input tables for MOVES from the PARAMICS Analyser output. COMPRESSED NATURAL GAS (CNG) By modifying the default AVFT (Alternative Vehicle Fuels & Technologies) table included in the MOVES run Spec file, the percentage of vehicles using different fuels and engine technologies can be changed. Table 1 shows part of the default AVFT table, which specifies the percentage of each type of vehicle using different fuels and engine technologies for each year. In Table 1, sourcetypeid represents vehiclee types (e.g., passenger car, transitt bus, single unit long-haul truck); modelyearid describes when the vehicle was manufactured; fueltypeid indicates the type of fuel being used; engtechid describes the engine technology used by the corresponding type of vehicle; fuelengfraction represents the percentage of vehicles with the specified sourcetypeid and modelyearid use a particular fuel and engine technology combination. For instance, the first two data rows in Table 1 suggest that for all passenger cars (sourcetypeid=21) manufacture ed in 1960, % of them use gasoline (fueltypeid=1) and conventional internal combustion engine (engtechid=1), while % of them use diesel fuel (fueltypeid=2) and conventional internal combustion engine. Another table (sourcetypeagedistribution table) in the MOVES input database defines the percentages of

9 Xie et al vehicles manufactured in each year. Given these two tables, the percentage of a specific type of vehicle (i.e., with vehicle, model year, fuel, and engine types defined) can be determined. Users can modify values in these default tables based on the real world situation as needed. Table 1. Default AVFT Table in MOVES sourcetypeid modelyearid fueltypeid engtechid fuelengfraction The first alternative fuel considered in this research is Compressed Natural Gas (CNG), which is mostly used in transit buses. To evaluate the impacts of using CNG in the case study network, the following test scenarios were considered. It was assumed that for transit buses made between 1990 and 2009, 10%, 20%, 30%, and 40% of them in the case study network use CNG, respectively. Also, a base scenario was included where a default approximately 6% of transit buses uses CNG. Table 2 shows the transit emission rates per vehicle miles traveled (VMT) for the five scenarios, where CNG_10 means that 10% of transit buses made between 1990 and 2009 use CNG. A total of five performance measures were considered, which were Carbon Monoxide, Oxides of Nitrogen, Sulfur Dioxide, Carbon Dioxide, and Total Energy Consumption. It can be seen that as the percentage of transit vehicles using CNG increases, most pollutant emission rates decrease. An exception is the CO emission rate, which increases. Another interesting finding is that the changing rates of pollutants are almost linearly proportional to the percentage of transit vehicles using CNG. Considering the lengthy computation time of the MOVES program, such a finding is both very interesting and useful. If this linear trend is true, users just need to calculate the emission results for two percentage values, and the emission results for other percentages of transit vehicles using CNG can be derived by interpolation. This can save considerable amount of computation time. Pollutants per VMT Table 2. Emission Rates for Transit Using CNG CNG Base CNG_10 CNG_20 CNG_30 CNG_40 Carbon Monoxide (g/vmt) Oxides of Nitrogen (g/vmt) Sulfur Dioxide (g/vmt) Atmospheric CO 2 (kg/vmt) Total Energy Consumption (Million BTU/VMT) Table 3 shows the percentage reductions of pollutant emission rates. Compared to the base scenario, CNG_40 can reduce the overall transit bus sulfur dioxide emission rate (in grams

10 Xie et al per VMT) by 34.35%. A close examination of Table 3 shows that CNG is effective in reducing oxides of nitrogen and CO 2 emission rates for transit buses. It can also slightly reduce the overall transit bus energy consumption. Interestingly, the case study result suggests that using CNG actually increases the transit bus carbon monoxide emission rate, which is in contradiction with the results reported in some existing literature (USDOE, 2003). This unexpected result probably can be explained by the different fuel formulation used in this study. By using the same fuel formulations employed in other studies, similar emission rates should be obtained. Table 3. Percentage Emission Reduction for Transit Using CNG Pollutants CNG_10 CNG_20 CNG_30 CNG_40 Carbon Monoxide Oxides of Nitrogen Sulfur Dioxide Atmospheric CO Total Energy Consumption ELECTRIC VEHICLES Similar to evaluating the impacts of using CNG, tests were also conducted to evaluate and compare the results of different percentages of electric vehicles. Since it is very rare for transit buses and trucks to be powered by electricity, we only considered different percentages of passenger cars using electricity. Similarly, the following test scenarios were considered. It was assumed that for passenger cars manufactured between 2001 and 2009, 10% (ELE_10), 20% (ELE_20), 30% (ELE_30), and 40% (ELE_40) of them in the case study network use electricity, respectively. A base scenario was included where there were no electric passenger cars in the case study network. Table 4 shows the emission rates per VMT for passenger cars for the five scenarios. The percentage emission reduction results compared to the base scenario is shown in Table 5. As can be seen, a clear linear trend exists for all five performance measures in Table 4. The results in Table 5 suggest that electric vehicles can significantly reduce the overall emission rates of sulfur dioxide, CO 2, and total energy consumption for passenger cars. They can also reduce the CO and oxides of nitrogen emission rates of passenger cars. Since the emission rates compared here are only for passenger cars, the results in Table 5 should be able to be generalized to other road networks even with different passenger car volumes. In addition, the results in the following two tables reflect the emission rates of pure electric passenger cars. For hybrid electric vehicles, the emission results may be quite different. Although hybrid electric vehicles are much more common than pure electric vehicles in the real world, the current version of MOVES cannot model hybrid electric vehicles. Hopefully the corresponding module can be added to MOVES in the near future.

11 Xie et al ETHANOL Table 4. Emission Rates for Passenger Cars Using Electricity Pollutants Electricity Base ELE_10 ELE_20 ELE_30 ELE_40 Carbon Monoxide (g) Oxides of Nitrogen (g) Sulfur Dioxide (g) Atmospheric CO 2 (kg) Total Energy Consumption (Million BTU) Table 5. Percentage Emission Reduction for Passenger Cars Using Electricity Pollutants ELE_10 ELE_20 ELE_30 ELE_40 Carbon Monoxide Oxides of Nitrogen Sulfur Dioxide Atmospheric CO Total Energy Consumption Fuel subtype and fuel formulation tables in MOVES database were used to facilitate the modeling of ethanol impacts. We created the E10 fuel formulation by modifying the default values provided in the MOVES fuel formulation table. The percentage of vehicle using E10 was changed by modifying the market shares of different fuels in the MOVES fuel supply input table. Similar to the previous analyses conducted for Electric and CNG, we considered five different scenarios (including a base scenario) for ethanol. The market share of E10 was changed from 10% to 40% in the increment of 10%. Runspec files were created in MOVES for each scenario and all files were run in a batch mode using the command line utility provided in MOVES. SQL scripts were developed to extract the total emissions generated by all types of vehicles from the MOVES output. Table 6 shows the performance measures per VMT generated by passenger cars and transit buses only. The percentage emission reduction results compared to the base scenario are shown in Table 7. The results in Table 7 indicate that while there is no change observed in atmospheric CO 2 and total energy consumption, sulfur dioxide is reduced by 33.0% and carbon monoxide is reduced by 11.0%, if the market share of E10 is 40%. With the increase in percentage of ethanol fueled vehicles, a negligible reduction in oxides of nitrogen is also observed.

12 Xie et al Table 6. Emission Rates for Transit and Passenger Cars Using Ethanol Pollutants Ethanol Base E10_10 E10_20 E10_30 E10_40 Carbon Monoxide (g) Oxides of Nitrogen (g) Sulfur Dioxide (g) Atmospheric CO 2 (kg) Total Energy Consumption (Million BTU) Table 7. Percentage Emission Reduction for Transit and Passenger Cars Using Ethanol Pollutants E10_10 E10_20 E10_30 E10_40 Carbon Monoxide Oxides of Nitrogen Sulfur Dioxide Atmospheric CO Total Energy Consumption SUMMARY AND CONCLUSIONS This research investigated the traffic emission impacts of selected alternative transportation fuels; ethanol, electricity and compressed natural gas, using MOVES. First, a review of existing studies on alternative transportation fuels and their environmental impacts was conducted. Unlike many previous studies that apply the life cycle model to analyze the full life cycle of fuels and vehicles, this study was conducted primarily from a transportation perspective and was concentrated on the emission impacts of using alternative transportation fuels in a small transportation network. In this research, MOVES and a microscopic traffic simulation model PARAMICS were utilized for investigating the traffic emission impacts, and were applied to a roadway network in Greenville, South Carolina. The selected road network was first coded into a PARAMICS network model and simulated. Detailed simulation results were summarized and fed into the MOVES tool. In this research, the impacts of ethanol, compressed natural gas (CNS), and electricity were investigated separately. For each alternative fuel, we estimated and compared the overall network emission results for different market shares of the corresponding alternative fuel in the case study network. Specifically, we considered 10, 20, 30, and 40 percents of market shares for each alternative fuel/alternative fueled vehicle. For each of the three alternative transportation fuels considered in this study, the experiments suggest a strong linear trend in the changes of most performance measures (i.e., emission rates and fuel consumption) with respect to the changes in the market shares. This linear trend may be the result of a linear relationship

13 Xie et al between the performance measures and the alternative fuels or linear prediction algorithms used in MOVES. The test results for transit buses demonstrated that, compared to the base scenario, switching 40% of transit buses from diesel to CNG can reduce the overall transit bus sulfur dioxide emission rate (in grams per VMT) by 34%. Additionally, CNG has shown to be effective in reducing the oxides of nitrogen and CO 2 emission rates for transit buses. It can also slightly reduce the overall transit bus energy consumption. Interestingly, the case study results suggest that using CNG actually increases the transit bus carbon monoxide emission rate, which is in contradiction with the results reported in some existing literature. This unexpected result probably can be explained by the different fuel formulation used in this study. The results also showed that electric passenger cars and buses can effectively reduce the overall emission rates of sulfur dioxide, CO 2, and total energy consumption. Increasing the market share of electric passenger cars and buses can also significantly reduce the overall CO and oxides of nitrogen emission rates. FUTURE WORK As discussed previously, there are basically two methods to use MOVES to quantify the emission impacts and fuel consumption of alternative transportation fuels, which are inventory method and emission rate lookup table method. The one used in this case study is the inventory method. This result suggests that for a simple network with around 240 road links, a single run of the inventory method takes about 90 minutes to complete using a workstation with 2 CPUs and 8GB RAM. If one wants to model larger road networks or simply to evaluate different fuel formulations/market shares/engine technologies, the computation time will become a serious issue. The MOVES emission rate lookup table method can generate a set of emission rates for different vehicles under various operating conditions. Such emission rates can be used in conjunction with simulation or transportation planning models repeatedly to estimate total emissions. In so doing, significant computation time can be saved. It would be interesting to conduct further research to compare the results of the inventory and emission rate lookup table methods and to see if there are significant differences between their estimated results. If the two methods produce approximately the same results, then the emission rate lookup table method should be recommended for future use. Also, it would be interesting to conduct research to find which of these two methods can generate results that are closer to the true values (i.e., real world emission results). Due to the lack of test data, the existing version of MOVES cannot model biofuels such as E85. MOVES cannot model some new engine technologies either, including hybrid engine, fuel cell engine, and hybrid-fuel cell. The MOVES database is being constantly updated. It is anticipated that the capability of modeling E85 and various new engine technologies will soon be added to MOVES. It is thus interesting to conduct follow-up research and to find out the emission impacts of E85 and the many new engine technologies. ACKNOWLEDGMENTS The authors would like to thank the James E. Clyburn University Transportation Center at South Carolina State University for supporting this research under Project No. R-09-JECUTC- AOEEITAF-CMET. All contents, opinions, and results expressed in this paper are solely of the authors.

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