Demonstrating a Bottom-Up Framework for Evaluating Energy and ...

Demonstrating a Bottom-Up Framework for Evaluating Energy and Emissions Performance of Electric Rail Transit Options Franklin E. Gbologah, Yanzhi Xu, Michael O. Rodgers, and Randall Guensler alternatives to assess their impact on air quality. A good analysis method for emissions must incorporate a robust measure of emissions activity specific to the technology under evaluation and must be sensitive to system characteristics such as passenger loading profiles, speed profiles, track profiles, ambient weather conditions, fuel technologies, and vehicle technologies. Ideally, the method must use a bottom-up approach to capture all these different characteristics. However, most emission analysis methods available to transit planners use a top-down approach based on parameters that represent systemwide averages. Although these approaches are often able to paint a broad picture of emissions from the system, they cannot easily be used to analyze emissions from the system’s component parts. For example, it may be difficult to analyze one service line out of a range of service lines within a rail transit system or to evaluate candidate transit options for new services from multiple options (1). A group of researchers at the Georgia Institute of Technology has developed the Public Transit Greenhouse Gas (GHG) Emissions Management Calculator in collaboration with the Oak Ridge National Laboratory, under the sponsorship of FTA. The calculator is built upon an emissions analysis framework for rail and bus technologies that uses a bottom-up approach based on travel activity. The framework uses second-by-second travel speeds and other operational and equipment characteristics to estimate a more specific and robust measure of emissions activity. The developed framework can serve as a tool for policy makers and transit planners to evaluate emissions from transit modes and unifies the analysis for rail technologies, including commuter rail, light rail, heavy rail and streetcars, and bus technologies, including compressed natural gas, hybrid electric, battery electric, plug-in hybrid electric, and fuel cell vehicles. The framework can be applied at both the system level and project level. A system-level application may yield results similar to those from existing top-down approaches; a project-level application will offer transit planners additional ability to perform detailed sensitivity analysis down to the individual trip level, albeit at the cost of greater data requirements. This paper focuses on the electric rail module of the GHG Calculator. Examples of electric rail technologies include heavy rail, light rail, and street cars, as opposed to diesel-electric technologies commonly used for commuter rail lines. The paper demonstrates the application of the rail module of the GHG Calculator to an electric rail transit system, in this case, the rapid transit system in Chicago, Illinois, (the “L”). A short discussion regarding two top-down frameworks is followed by a discussion of the newly developed framework, including the model calibration and validation processes. These discussions are followed by a presentation of the modeling results, conclusions, and a brief outline of future work.

Current frameworks for analyzing emissions performance of public transportation systems use top-down approaches that can often provide useful information at the network level but can be uninformative at the project level at which the influence of route and vehicle characteristics can significantly impact emission profiles of candidate transit options. This paper describes an alternative bottom-up framework that uses second-by-second travel activity data to estimate total power consumption and related emissions for propulsion purposes with application to electric rail transit systems. The model was developed and calibrated with data from Portland, Oregon, and was supplemented with activity data from Chicago, Illinois. The results showed a predicted 1% to 8% difference in expected power consumption relative to estimates derived from the national transit database. In addition, the results highlighted how the speed profile, configuration of the train in number of cars, and mix of power generation sources could significantly vary emissions performance across different service routes. The developed framework can serve as an important tool for a transit planner or policy maker to evaluate the emissions performance of electric rail transit options. This framework has the advantage of relevance at both the network and project levels. At the project level, users can easily perform detailed sensitivity analysis on aspects of transit services such as vehicle and fuel technologies, passenger loading profiles, train size, and track profile. This framework gives transportation planners a flexible and efficient tool for emissions performance analysis.

The question that often generates debate among transit planners is not the ability of mass transit systems to help communities overcome congestion and air quality challenges, but rather, which transit mode or technology offers the greatest benefit. In selecting a transit mode for a given corridor, planners have to consider a variety of factors including economics, service quality, vehicle technology, and the effect on the environment. The general public’s growing awareness of climate change and other environmental issues and the mitigating role that public transportation can play has drawn increased attention to the analysis of environmental impacts. A key step in evaluating the environmental impact of transit technologies and modes is the analysis of emissions from the candidate School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, NW, Atlanta, GA 30332-3055. Corresponding author: F. E. Gbologah, [email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2428, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 10–17. DOI: 10.3141/2428-02 10

Gbologah, Xu, Rodgers, and Guensler

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Existing Frameworks for Electric Rail Transit Trains There are, at present, two common top-down approaches that can be used to estimate emissions from both light and heavy rail transit trains (2–4). The first approach, illustrated in Equation 1, endeavors to calculate emissions per passenger mile. Although this calculation produces a useful activity measure, passenger miles may correlate poorly with actual electrical energy consumption as passenger loading on a service route can vary greatly from station to station. This concern is addressed in part by the second approach (Equation 2) which estimates emissions per seat mile. emissions per passenger mile =

emission factor p total electricity consumed total passenger miles p line loss factor

(1)

Travel activity data were requested from the rail transit agencies listed in the NTD. The Tri-County Metropolitan Transportation District of Oregon (TriMet), the public agency that operates mass transit in the Portland metropolitan area, responded with travel activity data for the Metropolitan Area Express (MAX) Blue Line, a light rail route, in Portland, Oregon. This line has 47 stations and covers about 32.5 mi in metropolitan Portland. These data were supplemented with second-by-second GPS speed and position data collected on the Brown and Orange Lines on the Chicago “L” heavy rail system as a part of the project. The Brown Line covers about 11.4 mi and has 28 stations from Kimball Station to downtown Chicago. Valid position and speed data were collected for an 8.2-mi section of this route, which included 18 stations. The Orange Line runs from Midway Airport to downtown Chicago. For this line, valid data were obtained for a 9.1-mi section, with 9 stations, of the overall 12.5 mi length, which has 17 stations. Figure 1 shows a map of the segments of the Brown and Orange lines where GPS data were successfully collected.

emissions per seat mile =

emission factor p total electricity consumed total vehicle miles p vehicle capacity p line loss factor

(2)

The emission factor (in grams per kilowatt-hour) for each pollutant of interest in a study area or region can be obtained from the U.S. Environmental Protection Agency’s eGRID and NET databases (2). The total electricity consumed, total passenger miles, and total vehicle miles for a rail transit system in a year are available from FTA’s national transit database (NTD) (2, 3, 5). The vehicle capacity can be obtained directly from the transit agency, the NTD, or the American Public Transit Association vehicle database (2, 6), which is published annually. Vincent and Walsh used a transmission line loss of 10% in their 2003 study of emissions from light rail transit and bus transit (4). However, Puchalsky argued later on that a 10% line loss factor is more typical of residential electricity use than of transit system use (3). He pointed out that rectifying substations of transit agencies receive electricity at a much higher voltage range; therefore, a more reasonable line loss factor applicable to rail transit systems is 0.97, that is, a 3% transmission loss from power plant to rectifying substation. Additional losses due to rectification are already accounted for in the data reported in the NTD. Developed Framework for Electric Rail Transit Trains Emissions from an electric train are emitted upstream at the power generation plant. In most cases the power generation plants are located at great distances from the rail tracks and are transmitted to the point of use over power lines. This transmission process results in some losses that need to be accounted for in any life-cycle analysis. However, in the developed model transmission losses have not been accounted for because the model is intended to estimate the power requirement for propulsion (including hotel load) as is reported by the transit agencies to the NTD, and upstream transmission losses need to be accounted for separately. Data Sources Wherever possible this study relied on published data provided by FTA in the NTD with supplemental information from third parties, as necessary.

Model Description The developed framework and model for estimating emissions from electric rail transit are a subcomponent of the larger Public Transit GHG Emissions Management Calculator that includes other frameworks for estimating emissions for bus transit and diesel rail transit systems. The diesel train model was reported at the Third International Conference on Urban Public Transportation Systems (1). The electric rail transit framework, which is the focus of this study, can be broken down into five main modules: data reporting, instantaneous rolling tractive effort, starting tractive effort, power recovery, and emissions analysis. Data Reporting Module Users input the required information about a specific trip or rail transit service into the data reporting module. This information includes route-specific information such as station or waypoint names, mileposts, and elevations used to generate a track grade profile. Additional information regarding the rail vehicle, ambient weather, and track infrastructure are also recorded. Some of the rail vehicle information collected includes the weight of an empty railcar; number of axles per car; number of cars per train; passenger and seating capacities per car; power recovery; and heating, ventilating, and air conditioning operation. Ambient weather information includes temperature and indication of dry or wet weather conditions and snow and ice. The track infrastructure data collected include the track type and track condition. Instantaneous Rolling Tractive Effort Module The instantaneous rolling tractive effort module uses the second-bysecond train speed data to compute the instantaneous energy requirements for the moving train. Each instantaneous computation is made up of the train’s unit moving resistance on level grade, unit resistance to acceleration and deceleration, and the hotel load. The unit moving resistance and the unit resistance to acceleration are given in Equation 3. The first four terms in the first parentheses are often referred to as the modified Davis equation (7), which estimates the unit moving resistance of the train on a level grade. However, when the train is on a positive track grade there is an additional resistance of 20 lb/ton per percentage grade (7, 8). This additional resistance is shown as the

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Transportation Research Record 2428

(b)

(a) FIGURE 1 Map of GPS data collection in Chicago on (a) Brown Line and (b) Orange Line.

last term in the first parentheses. The term in the second parentheses refers to unit resistance to acceleration. 2 2   KV 22 20  V −V  R =  0.6 + + 0.01V2 + + 20 p θ + 70  2 1   p γ  L  wp w pn p  

(3)

where wp = weight per passenger railcar axle (tons), V2 = current instantaneous speed of train (mph), V1 = previous instantaneous speed of train (mph), K = train drag coefficient, np = number of axles per passenger railcar, θ = positive track gradient at the instantaneous location (%), γ = adjustment factor, L = distance moved in a second (ft), and R = unit resistance to moving train (lb/ton). The modified Davis equation is applicable to train speeds of up to 60 mph; at greater speeds it tends to overestimate and Tottem’s revision needs to be applied (7). In spite of this limitation, the authors believe the equation will be applicable to almost all rail transit operations in the U.S. today. Also, Hay noted that other researchers have found that the Davis equation tends to overestimate and he therefore recommended that an adjustment factor of 0.8 to 0.85, for conventional post-1950 cars, should be applied to the modified Davis equation (7), that is, the first four terms in the first parentheses of Equation 3. In this study, the adjustment factor was applied to the modified Davis equation as well as to the additional unit resistance attributable to track grade and to the unit resistance attributable to acceleration because they are all based on Davis’s work. Thus, to calibrate the model, a sensitivity analysis was performed on the adjustment factor (γ). Addition-

ally, in theory, more effort would be required to stop the train than to accelerate it. However, the unit resistance attributable to acceleration and that attributable to deceleration were estimated in the same way. Next, the maximum hotel load demand per car was estimated as 25 kW for continuous operation. The heating, ventilating, and air conditioning settings were simplified to three levels: normal, high, and maximum. The normal level functions at a third of the maximum hotel load demand per car, the high level operates at two-thirds of the maximum hotel load demand, and the maximum level operates at full hotel load demand. Starting Tractive Effort Module The starting tractive effort module computes the additional tractive effort required to move the train from 0 speed, such as when it starts moving after stopping for passengers to alight and board at intermediate stations. This module considers five factors that contribute to the starting tractive effort. These factors and their contributing unit resistances are as follows: 1. Track grade resistance. The resistance is 20 lb/ton of train weight per percentage of grade. Only positive grades contribute to this resistance. 2. Bearing resistance. The resistance is 10 lb/ton at 50°F. For every degree F below 50°F, the resistance increases by 0.1 lb/ton. Likewise, the resistance decreases by 0.1 lb per degree F above 50°F. 3. Track resistance. For 130-lb rail there is no resistance. For 115-lb rail the resistance is 1 lb/ton and for 100-lb rail it is 2 lb/ton. 4. Weather resistance. For wet rails the resistance is 2 lb/ton and for rails with ice or snow the resistance is 10 lb/ton. 5. Track condition. For tracks with good rails and crossties there is no additional resistance. However, for poor rails and fair crossties


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Finding published estimates of these figures for passenger rail systems was difficult. The values chosen were recommended by the Chattahoochee Locomotive Company (9), which deals primarily with freight trains. However, the authors believe that the given resistances per weight of train are good estimates.

cific; therefore, they vary according to the power generation mix for the state in which the transit service is located. The GHG Calculator automatically selects the appropriate emissions rates from an embedded lookup table once the user specifies the home state of the transit service for which the analysis is conducted. This approach allows the model to make accurate emissions estimates regardless of the spatial distance between the point of generation and the point of use of electrical power.

Power Recovery Module

Model Calibration

The developed framework provides users an option to include power recovery as an adjustment to the total estimated power for trains with regenerative braking capability. According to data published by the International Union of Railways in 2002, the potential energy recovery from DC and AC transit systems will be between 5% and 10% (10, 11). However, as recently as August 2012 Siemens had installed regenerative braking technology, which saves 30% energy on suburban Mumbai trains in India (12). Other intercity trains built by Bombardier have also shown up to 30% energy recovery (13). These figures underscore the need for a bottom-up approach because available regenerative braking technology on trains can significantly affect power consumption estimates used in emissions analysis of rail transit systems.

The parameter requiring calibration is the model’s adjustment factor. In ideal circumstances, calibration would be done by first running the model with travel activity data from transit agencies with different values of the adjustment factor. Then, the value that consistently gave the best estimate of electrical power consumption for the agencies would be selected. However, obtaining both travel activity data and related power consumption data was not possible. As noted above, travel activity data from only two transit agencies were available at the time this paper was written. Therefore, the model was calibrated with the most complete data set, which was from the MAX Blue Line in Portland. In addition, before the final formulation of the model was chosen, four forms (M1 to M4) were evaluated. The differences between these forms relate to the application of the adjustment factor, which was recommended by Hay (7). His original recommendation was to apply the factor only to the modified Davis equation, that is, the first four terms in Equation 3. However, as noted previously, all of the other terms in the model were selected on the basis of Davis’s work so the adjustment factor was applied in different combinations to the terms. In Model M1 the factor was applied only to the modified Davis equation, in Model M2 the factor was applied to both the modified Davis equation and the term for additional unit resistance caused by track grade, in Model M3 the factor was applied to both the modified Davis equation and the term for unit resistance caused by acceleration or deceleration, and in Model M4 the factor was applied to all of the terms. NTD’s current version, NTD 2011, has data from TriMet’s MAX light rail system but not specific data on the Blue Line. However, the data show that, on average, TriMet’s MAX light rail system (which covers six different lines) consumes about 13.57 kilowatt-hour (kW-h) per vehicle mile (VM) and 0.11 kW-h per seat mile (SM). Therefore, the combination of model and adjustment factor that best

the resistance is 2 lb/ton and for poor rails and poor crossties the resistance is 7 lb/ton.

Emissions Analysis Module The emissions analysis module is used to estimate the related pollutant emissions (in grams) by using the estimated total electrical power consumed for propulsion (in kilowatt-hours), including hotel load, and the emission rate (in grams per kilowatt-hour) in the formula in Equation 4. total emissions = energy consumption p emission rate

(4)

The pollutants analyzed in the framework are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), carbon monoxide (CO), volatile organic compounds (VOC), nitrogen oxides (NOx), particulate matter less than 2.5 µm (PM2.5), particulate matter less than 10 µm, (PM10), and sulfur dioxide (SO2). The emissions rates are obtained from the most recent version of the Greenhouse Gases Regulated Emissions and Evaluation Model in Transportation developed by Argonne National Laboratory. The emissions rates are location spe-

TABLE 1 Estimated Electrical Power Consumption for the MAX Blue Line Model M1 γ 0.850 0.800 0.790 0.785 0.782 0.781 0.780 0.750

Model M2

Model M3

Model M4

kW-h/SM

kW-h/VM

kW-h/SM

kW-h/VM

kW-h/SM

kW-h/VM

kW-h/SM

kW-h/VM

0.13 0.13 na na na na na 0.13

16.96 16.91 na na na na na 16.85

0.13 0.13 na na na na na 0.13

16.63 16.47 na na na na na 16.31

0.12 0.11 na na na na na 0.11

15.00 14.29 na na na na na 13.59

0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11

14.67 13.86 13.70 13.62 13.57 13.55 13.54 13.05

Note: na = not applicable.

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TABLE 2 Parameters Used to Set Up Models Values, by Line Parameter

MAX Blue

Chicago Orange

Chicago Brown

Weight of empty car (tons) Number of axles per car Train drag coefficient Seating capacity per car Percentage loading (peak period) Percentage loading (off-peak period) Average percentage loading (day) Number of cars per train (peak period) Number of cars per train (off-peak period) Maximum hotel load per car (kW) Car HVAC operating level Average weight per passenger (lb) Potential power recovery rate (%)

54.5 6 0.07 64 na na 43.0 2 2 25 Normal 150 0

27.15 4 0.07 49 87.5 25.0 45.0 7 4 25 Normal 150 0

27.15 4 0.07 49 87.5 25.0 45.0 6 4 25 Normal 150 0

Ambient temperature (°F) Weather condition Track type Track condition

74 Dry 115-lb rail Good rails and crossties



estimated these values was sought for the calibration analysis. Table 1 presents the output from the calibration analysis. Table 2 shows the parameters from the TriMet system that were used to set up the models. As shown by the estimated electrical power consumption in Table 1, the best model formulation is Model 4 (M4) and the best adjustment factor is 0.782. Model Validation The data collected on the Brown and Orange Lines on Chicago’s “L” system were used to validate the calibrated model. By using the “L” system’s information in the 2011 NTD, it was possible to determine that the average power consumption rate was 36.44 kW-h/VM and 0.13 kW-h/SM. These figures represent averages for the eight lines operated by the Chicago Transit Authority. The Orange Line operates seven passenger cars per train during the peak period and four passenger cars per train during the off-peak period; the Brown Line has six passenger cars per train and four passenger cars per train respectively for the two periods. Therefore, for each of these lines the electricity consumption was estimated for both the peak period and the off-peak period. This estimation required determining the average loading per car for each period. First, the average systemwide load per car was estimated by dividing the reported total passenger miles by the total passenger car revenue miles. Next, a factor for each period was estimated to adjust the estimated average load per car. The 2011 NTD shows that for a typical weekday there were 703,327 passenger trips—345,050 for the peak period and 358,277 for the off-peak period. The combined morning and evening peak periods cover 6 h. On average, trains across the eight service lines on the “L” operate for about 22 h/day. Therefore, normalizing these passenger trip figures over a 6-h period and comparing the normalized peak and off–peak period trips to the normalized typical day trips made it possible to estimate that the peak period factor

was 1.96 and the off–peak period factor was 0.56. When multiplied with the average system load per car, the estimated loading is about 87.5% in the peak period and about 25.0% loading in the off-peak period. The parameters used to set up the models are provided in Table 2. Table 3 presents the power consumption estimates from the model. Because only system-level information as expected data was available to compare with the model’s output, average estimates were calculated for the two lines. The data in Table 3 show that the calculated averages for the two lines are 36.01 kW-h/VM and 0.14 kW-h/SM. These values are very close to the intended expected values from NTD; they differ by only approximately one and eight percentage points, respectively. Discussion of Results The estimated power consumption values in Table 3 show that although the model’s output at the system level is significantly close to the expected values derived from the NTD, the model’s estimates

TABLE 3 Estimated Electrical Power Consumption for Brown and Orange Lines in Chicago Line and Period Brown Peak Off-peak Orange Peak Off-peak 2-line average

kW-h/VM

kW-h/SM

41.32 25.67

0.14 0.13

50.28 26.75 36.01

0.15 0.14 0.14

Speed (mph)


60 50 40 30 20 10 0

0

15

500

1,000

1,500

2,000

2,500

Operation (s) FIGURE 2 Speed profile for Orange Line.

Speed (mph)

of energy consumption per vehicle mile for the peak and off-peak periods for both the Brown and Orange Lines are significantly different from the expected values—between 13% and 38% for the peak period and between 27% and 30% for the off-peak period. Distinguishing peak period energy consumption from off-peak energy consumption is an important consideration because most transpor tation projects related to congestion mitigation and emissions reduction focus on peak period system performance. This observation supports the authors’ view that a bottom-up approach, such as the developed framework introduced here, will provide better information to policy makers, enabling them to perform sound and effective comparisons of transit options. Furthermore, the data in Table 3 show that, given equal passenger loading profiles and equal vehicle seating capacity, the Orange Line consumes about 7% and 8% more electricity per seat mile than does the Brown Line for the off-peak and peak periods, respectively. Once again, such information cannot be easily extracted from other top-down approaches. Further analysis revealed that this higher consumption rate was attributable to differences in speed profiles on the routes. For example, the maximum speed on the Orange Line was more than 50 mph; maximum speed on the Brown Line was approximately 47 mph. Figures 2 and 3 show the speed profiles on the Orange and Brown Lines, respectively. The model’s ability to reflect energy consumption differences between lines is important in the current environment in which most transit planning occurs at the corridor level rather than the system level. Additionally, the developed bottom-up framework is able to account for different train consists (number of cars) in analyses of air quality impact. This important attribute of the model can be seen from the output data from the peak period analysis for Chicago “L” trains shown in Table 3. A comparison of the estimated power consumption per vehicle mile shows that the Orange Line train consumes

60 50 40 30 20 10 0

0

500

1,000

about 22% more electricity. However, when the comparison of the two trains is per seat mile, the model accounts for the extra car in the Orange Line train consist, showing that the difference in power consumption between the two trains is only about 7%. In both the calibration and validation processes for all of the analyses, estimated power consumption rather than emissions was used. This choice was made because emissions are dependent on total power consumed for propulsion (including hotel load) in electric trains. Additionally, power consumption values are more readily available and easier to use in these processes. However, in the event that some readers might find it helpful to have some information on the related emissions estimates, the estimated emissions for the peak period analyses on the Chicago “L” trains used in validating the model are shown in Table 4. Additionally, Table 5 presents the estimated emissions for the TriMet MAX Blue Line train. The developed framework can be used to analyze multiple transit alternatives or technologies by making one copy of the model file for each alternative and comparing the various outputs from the model. If all the alternatives analyzed get their electricity from the same source or combination of sources, then their estimated power consumption per seat mile can be directly compared. Otherwise, the comparison must be made with the estimated emissions per seat mile. For example, the data in Tables 1 and 3 show that the Chicago “L” Brown Line and Orange Line trains analyzed in this study consume about 27% and 35% more kilowatt-hours of electricity per seat mile, respectively, than does the MAX Blue Line train from Portland. In addition, a comparison of their estimated emissions per seat mile for the various pollutants presented in Tables 4 and 5 shows that, on average, they produce about 300% and 335% more emissions, respectively, than does the MAX Blue Line train. This difference in emissions is attributable to Portland’s use of a cleaner mix of electrical power generation

1,500

Operation (s) FIGURE 3 Speed profile for Brown Line.

2,000

2,500

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TABLE 4 Estimated Peak Period Emissions for Chicago “L” Trains Used in the Study Line

CO2 (kg)

CH4 (g)

N2O (g)

CO (g)

0.23 0.28

0.33 0.40

17.62 21.44

0.0008 0.0008

0.0011 0.0012

VOC (g)

NOx (g)

PM2.5 (g)

PM10 (g)

SO2 (g)

Emissions per VM Brown Orange

20.01 24.35

0.37 0.45

14.21 17.29

1.00 1.21

1.83 2.22

0.0034 0.0035

0.0062 0.0065

46.40 56.46

Emissions per SM Brown Orange

0.0681 0.0710

0.0599 0.0625

0.0013 0.0013

0.0483 0.0504

0.1578 0.1646

TABLE 5 Estimated Emissions for TriMet MAX Blue Line Train Used in the Study Unit

CO2 (kg)

CH4 (g)

N2O (g)

CO (g)

VOC (g)

NOx (g)

PM2.5 (g)

PM10 (g)

SO2 (g)

VM SM

2.24 0.0175

0.09 0.0007

0.02 0.0002

1.97 0.0154

0.04 0.0003

1.93 0.0151

0.11 0.0009

0.20 0.0016

2.53 0.0198

sources than Chicago uses. The ability of the model to capture the influence of different mixes of power generation sources across different regions is very important in assessing a transit alternative’s impact on air quality. Last, although the model was calibrated against the TriMet’s MAX light rail in Portland, it also worked well with the “L” heavy rail in Chicago. Similar levels of accuracy for other transit systems are expected as long as there is no significant difference in the propulsion control technologies, aerodynamics of the trains, and track infrastructure. For example, the model may not apply well to systems using magnetic levitation. Additionally, the accuracy of analyses of electric trains with regenerative braking technology may be reduced if the right power recovery rates are not used.

Conclusions This paper demonstrated a bottom-up framework that transit planners and policy makers can use to compare the emissions performance of electric rail transit options to make well-informed decisions when considering candidate options for new rail transit services or when evaluating the performance of existing services for planning purposes. Unlike other conventional top-down frameworks, the developed bottom-up approach offers users flexibility and ease of use in performing important sensitivity analysis concerning the influence of vehicle, operation, infrastructure, and ambient weather characteristics on the emissions performance of electric trains. Also, the model unifies the analysis of light rail, heavy rail, and streetcars into one simple framework. The model was calibrated with data from TriMet in Portland and validated with data from Chicago.

Limitations and Future Research The main limitation is the current limited availability of secondby-second train travel speeds and related power consumption data to calibrate the model. To date, the model has only been applied

with data from Portland and Chicago; the robustness of this calibration should be tested with data sets from other transit agencies. The authors are continuing efforts to gather more data from other transit agencies to determine if the model’s calibration requires additional improvements. Another potential limitation of the model relates to the adjustment factor suggested by Hay (7) because it is intended to account for differences in railcar design. Current railcar stocks can be grouped into two main types on the basis of shape. One type, usually made up of older stock, has straight vertical edges at the front and back; an example is the Siemens SD100. The second type, which is predominantly newer stock, has a more curvilinear front and back design; an example is the Siemens S70. These two designs can produce different aerodynamic characteristics, which might result in two different adjustment factors for the two groups of railcars. All of the trains used in this study were of the first type. More data will be required to assess the impact of different railcar front and end designs. The authors are collecting data on diesel rail systems, such as commuter rail, to use in writing a paper similar to this one, which will demonstrate the calibration and validation of the model for estimating diesel rail transit emissions. Additionally, they plan to test the model for streetcars.

Acknowledgments The authors thank the FTA and Oak Ridge National Laboratory, which cosponsored this research. The authors also thank Jason Grohs of TriMet; without his efforts to obtain the necessary data, this study would not have been possible.

References 1. Xu, Y., F. Gbologah, K. Cernjul, A. Kumble, R. Rodgers, and R. Guensler. Comparison of Fuel-Cycle Emissions per Passenger Mile from Multiple Bus and Rail Technologies. Proc., Third International Conference on Urban Public Transportation Systems, Paris, 2013, pp. 204–216.


2. Messa, C. A. Comparison of Emissions from Light Rail Transit, Electric Commuter Rail, and Diesel Multiple Units. In Transportation Research Record: Journal of the Transportation Research Board, No. 1955, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 26–33. 3. Puchalsky, C. M. Comparison of Emissions from Light Rail Transit and Bus Rapid Transit. In Transportation Research Record: Journal of the Transportation Research Board, No. 1927, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 31–37. 4. Vincent, B., and B. Walsh. The Electric Train Dilemma: Clean Transportation from Dirty Electricity? June 18, 2003. www.gobrt.org/Rail BRTAirQualityAnalysis.pdf. Accessed July 7, 2013. 5. FTA, U.S. Department of Transportation. National Transit Annual Databases. http://www.ntdprogram.gov/ntdprogram/data.htm. 6. APTA. Public Transportation Vehicle Database. 2011. http://www.apta. com/resources/bookstore/Pages/ProductProfile.aspx?prd_key=0b28f3c39757-42b1-94c0-0a151c79d4f8. 7. Hay, W. W. Railroad Engineering, 2nd ed. John Wiley & Sons, New York, 1982. 8. Gregory, G., and N. Debbie. Review of Regional Locomotive Emission Modeling and the Constraints Posed by Activity Data. In Transportation Research Record: Journal of the Transportation Research Board,

17

No. 2117, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 24–32. 9. Chattahoochee Locomotive Company. Calculations. http://railspur. com/Pages/Calculations.pdf. Accessed Feb. 25, 2013. 10. International Union of Railways. Regenerative Braking for 50 Hz, 25 Kv Systems. Oct. 9, 2002. http://www.railway-energy.org/tfee/index.php? ID=220&TECHNOLOGYID=103&SEL=210&SEKTION=Sec3_detail #a_Environmentalcriteria. Accessed Feb. 25, 2013. 11. International Union of Railways. Regenerative Braking for DC Systems. Oct. 9, 2002. http://www.railway-energy.org/tfee/index.php?ID=220 &TECHNOLOGYID=103&SEL=210&SEKTION=Sec3_detail#a_ Environmentalcriteria. Accessed Feb. 25, 2013. 12. Sudhir, M. D. Braking Green for Mumbai Trains. Aug. 15, 2012. http:// blog.siemens.co.in/?p=4559. Accessed July 26, 2013. 13. Bombardier. Bombardier’s Environmental Technologies Help Delhi Metro Achieve World-First Under United Nations-Backed Climate Change Initiative. June 7, 2011. http://csr.bombardier.com/en/media-center/ csr-news/25-bombardiers-environmental-technologies-help-delhimetro-achieve-world-first-under-united-nations-backed-climate-changeinitiative. Accessed July 26, 2013. The Transportation and Air Quality Committee peer-reviewed this paper.