the operating-mode fractions inputs needed for MOBILE5B and modal emissions models. The development and revision of state implementation plans (SIPs).
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Soak Distribution Inputs to Mobile Source Emissions Modeling Measurement and Transferability Mohan M. Venigalla and Don H. Pickrell Soak period distribution is a key input for modeling the emissions factors for mobile sources. Methods for deriving soak period distributions from travel survey data are discussed. Data from the 1995 Nationwide Personal Transportation Survey (NPTS) were analyzed to derive soak times. Detailed analyses were conducted to relate soak time variability to geographic, trip-purpose, and time-of-day variables. The findings reinforce the prevailing general guidance on inputs to modeling the emissions from mobile sources. That is, whenever possible, local data should be used to derive soak distribution inputs to the emissions models. Alternatives are offered to accurate but more expensive local surveys as well as to the roughly aggregate and potentially inaccurate national defaults on soak period inputs to emissions factor models such as MOBILE6. On the basis of a detailed statistical analysis, a grouping scheme is devised to consolidate soak distribution inputs by time period. The grouping scheme will enhance the utility of survey data in deriving the soak distributions and reduce the effort in providing soak distribution inputs to MOBILE6. The soak periods derived from the NPTS data also can be used for deriving the operating-mode fractions inputs needed for MOBILE5B and modal emissions models.
The development and revision of state implementation plans (SIPs) and the conformity determination of transportation projects for monitoring transportation-related air quality require that emissions inventories be taken for various scenarios and compared with a baseline scenario. The emissions inventory studies are performed by numerous modeling exercises that involve air quality models as well as transportation models. Regional emissions inventory studies routinely use such regulatory models as the U.S. Environmental Protection Agency’s (EPA’s) MOBILE model (or the EMFAC model in California) to obtain emissions factors. These emissions factors are applied in conjunction with the transportation variables to calculate total emissions under a given set of conditions (1). On the other hand, the microscopic models such as the Comprehensive Modal Emissions Model (CMEM) and MEASURE, which are relatively new to the emissions modeling landscape, are used for developing project-specific emissions estimates. These microscopic models account for vehicle operating modes such as acceleration, deceleration, cruise, and idle in a more detailed manner than the macroscopic models do. The EPA develops and supports regulatory emissions models that will be used in the development of SIPs and the conformity process. Various parameter estimates on vehicle and travel activities, fleet mix, M. M. Venigalla, George Mason University, Civil, Environmental, and Infrastructure Engineering Department, MSN 4A6, Fairfax, VA 22030-4444. D. H. Pickrell, Volpe National Transportation Systems Center, DTS 49, 55 Broadway, Cambridge, MA 02142.
emissions control programs in place, and so on are required as inputs to these models. Version 6 of the MOBILE model (MOBILE6) differs significantly from the earlier versions in several ways. The most notable difference is the separation of start and running emissions, which underscores the importance of start emissions. To accurately model start-related emissions in MOBILE6, soak distribution variables were introduced. In addition, MOBILE6 requires many more vehicle, travel activity, and fleet-mix parameters than versions 5A and 5B (MOBILE5x) (2). A vehicle is considered to be “soaking” if its engine is not running. The length of time between two successive events that define an engine turn-off and ignition—known as the cold-soak period or simply the soak period—determines whether an engine start is a cold or hot start (2). Cold- and hot-start fractions of trips determine the vehicle miles of travel (VMT) mix operating in cold transient, hot transient, and stabilized modes, which are collectively known as (engine) operating-mode fractions. (Here, operating mode refers to the state of the engine and it is not to be confused with vehicle operating mode, which refers to state of the vehicle’s motion, such as acceleration, deceleration, cruise, and idle. The MOBILE model is mainly associated with engine operating modes, whereas the modal emissions models such as CMEM and MEASURE are associated with both engine and vehicle operating modes.) The operating-mode fractions are key input variables for the MOBILE5x models. In general, the higher the VMT operating in cold transient mode, the higher the concentrations of carbon monoxide and hydrocarbons in the inventory (3). Soak time distribution, which determines the cold- and hotstart fractions, is, therefore, a key variable that indirectly affects MOBILE5x inputs. Start-related inputs are also important to the modal process of emissions modeling. For example, CMEM requires the number of cold starts as an input in the modeling process (4). The MOBILE6 model, on the other hand, requires soak distributions as direct input (2). Within MOBILE6, the emissions value of an average vehicle start is calculated as the sum of the product of the start-emissions effects associated with each time bin and the corresponding proportion of soak-length activity. The product of this average vehicle start emissions with the number of starts per day represents the start-emissions level. Estimates of hourly start-emissions level values developed by using a set of default soak-time distributions representing national average conditions are available in MOBILE6 (5, 6 ). Therefore, soak distribution inputs are vital to emissions modeling exercises using MOBILE5x and 6. The overall dynamics of the modeling environment not only affected the ultimate form of the soak distribution inputs to the MOBILE model but also presented certain challenges to the modeling community. Addressed in this research are two important issues related to soak period inputs:
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• What values are to be used for soak distribution parameters, how may they be derived, and what is the transferability of these parameter values from one region to another? • How may these inputs be consolidated in a meaningful manner so as to minimize the effort while preserving accuracy? In addition to the cold-soak distribution, MOBILE6 requires inputs on hot-soak activity. However, the research presented here deals only with cold-soak distributions.
RESEARCH NEED Soak time distribution is a key variable in determining trip activity inputs to all versions of the MOBILE model currently in use. Several researchers have developed methodologies to derive inputs related to start-mode as well as operating-mode fractions for use with MOBILE5A and MOBILE5B (7–11). These studies derived several trip operating-mode fractions as alternatives to the federal test procedure (FTP) mode mix based on detailed analyses of personal travel data. The start- and operating-mode fractions had been derived previously (2, 7 ) from personal travel information in 1990 Nationwide Personal Transportation Survey (NPTS) data. After determining the percentages of start mode as cold starts and hot starts, the percentages of VMT operating in different modes were derived by trip purpose and by time period. The VMT weighted operating-mode fractions derived from these start-mode fractions indicated a significant difference from the FTP operating mode mix. The FTP operating mode mix generally underestimates the portion of travel in cold transient mode. Also, the percentage of VMT in cold transient mode was observed to decrease with the increase in the size of the urban area. Even though cold-soak times of each trip were used to determine the cold- and hot-start fractions, these studies focused on using the analysis results for emissions modeling with MOBILE5x. Traffic assignment methods were modified to derive link-specific operating-mode fractions for high-resolution emissions inventory (8, 10). These studies mainly focused on deriving soak time or start inputs for the MOBILE4x and 5x models; because MOBILE6 will soon replace the earlier versions, soak period inputs pertinent to modeling methods embedded in the MOBILE6 model must be derived. Some EPA documents address MOBILE6 soak period input needs (2, 5, 6), but the discussion in these documents focuses mainly on the input formats and the manner in which the variables are used internally in the model. The guidance from these documents and other EPA sources is unequivocal and advocates, to the extent possible, the use of locally derived input parameters for emissions modeling. When local data are not available, data from similar geographic regions should be used. If data from similar regions are not available, then national defaults may be used, with some degree of caution. Although the EPA guidance indicates what kind of data is appropriate to use with MOBILE6 model, it does not address the methods for deriving these data, nor does it address the transferability or portability of input parameters across various regions. These deficiencies are not specific to soak period inputs but apply to many input parameters in general. Nair et al. identified the need for deriving soak period inputs and addressed this gap by demonstrating the development of empirical relationships for the Dallas–Fort Worth, Texas, area through the use of local household travel survey data (12). However, these empirical models are not flexible and not necessarily transferable across geographic regions, times of day, and trip purposes. Municipalities can
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successfully implement the methodology proposed by Nair et al. by deriving similar empirical models for local conditions. These efforts would require not only extensive local travel survey data but also that the analysts possess specialized analytical skills to process the data. When such data and resources are not available, agencies are faced with a decision between adopting transferable parameters (such regional or national default values) and making crude assumptions pertaining to these key input data. When local data or data from comparable regions are available, the analysts must choose between developing mathematical models and deriving aggregate parameters from lookup tables, which list in one chart the input parameters derived from earlier studies. Lookup tables allow researchers to use reported composite values for MOBILE model input parameters. The main advantages of mathematical models are that they are compact, portable, and easy to apply. The main disadvantage is that many agencies lack trained personnel to develop models. However, developing and applying lookup tables in the context of emissions inventory modeling is relatively easy. The main disadvantage of tabulated parameter values is the lack of maneuverability, especially when the data tables are large. For example, the MOBILE6 inputs related to soak distribution are to be provided as a set of values for 70 soak durations (or soak intervals) for each of the 24 h of weekday and 24 h of weekend day (2). For a very detailed microscopic emissions analysis, the user would have to input 3,360 values. Hot-soak activity inputs, on the other hand, require inputs for 14 time periods and 60 1-min intervals—840 values. Arranging the soak periods in the required format is a significant challenge in itself. Even though most studies may not require the provision of inputs to this high degree of resolution, providing any manual inputs related to soak period activity is a laborious task. However, this disadvantage can be addressed by storing the large data sets in database formats. The necessary data items can be extracted by using simple query programs built around the database. Transferable input parameters on soak period and other travel activity data may be derived at the local, regional, or national level from available transportation data sources, most notably, travel survey data. Because a good majority of travel surveys are conducted for purposes other than emissions modeling, the usefulness of the resulting data for deriving travel activity data for emissions modeling is of concern. Although newly conducted surveys can be modified to seek the information necessary for emissions modeling needs, it does not obviate the need for additional resources in analyzing these data for emissions modeling purposes—not to mention the need for additional resources to continually update the information. In recent years much research emphasis was given to improving modeling methods and developing new-and-improved models. Unfortunately, research related to input data for these methods and the models is lagging. The research presented in this paper addresses methods to derive transferable soak period inputs to the models of emissions from mobile sources. Specifically the research effort seeks to accomplish four tasks: 1. Derive cold-soak periods for each vehicle trip from the 1995 NPTS data, 2. Identify statistically significant variables that explain the variances in soak times, 3. Develop criteria for consolidating soak period inputs, and 4. Enumerate guidelines for deriving and applying soak period inputs to modeling the emissions from mobile sources. Although soak time applies only to cold activity in this paper, the procedure discussed may be used to analyze hot-soak activity data
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inputs. Also, the discussion focuses on using the soak times for deriving soak distribution parameters for MOBILE6. Operating-mode fraction inputs related MOBILE5B and the start-mode inputs related to CMEM were studied but are not discussed.
• Separating highway trips from transit trips, • Isolating vehicle trips from person trips, • Preserving sample characteristics of the NPTS data in the analysis data, and • Checking for consistency and considering additional matters.
DERIVING SOAK TIMES FROM 1995 NPTS DATA
Because trip chains can be reconstructed only for trips made in household vehicles, it is important to separate highway trips from transit trips and then isolate vehicle trips from person trips. For example, household vehicles made only 320,646 trips out of 409,025 trips in the travel-day data. Nonhousehold vehicles or transit were used for making the remainder of the trips. Consequently, more than 88,000 trips in the database (21.6%) cannot be used for soak period analysis. The median percentage of trips that used household vehicles for travel at the MSA level is about 79%. At the high end, household vehicles accounted for more than 88% of the trips made by residents in the Ft. Lauderdale, Florida, MSA. At the low end, in New York City, only about 45% of the trips were made by household vehicles. A closer look at the data indicated that a majority of the travel-day trips in the New York metropolitan area were represented by transit modes. When discarding such a high percentage of trips made by nonhousehold vehicles, it is important to ensure that the sampling characteristics of the trip data are preserved in the analysis data. A root-mean-square (RMS) analysis of the trip weights of the sample containing only the trips made by household vehicles vis-à-vis the trip weights of the overall sample was conducted at the MSA level. The results indicate a potential RMS error of 2.8% in the weights of the household vehicle trip sample. Even though an RMS error of 2.8% is tolerable, to reconstruct vehicle trip chains for our purposes (i.e., computing soak times), all person trips that represent a duplicate vehicle trip must be discarded. When only the unique vehicle trips are retained in the final analysis database, the overall sample size is reduced to slightly more than 252,300 trips. The guidance on adjusting the weights when eliminating certain person trips from the NPTS data is not clear. To identify maximum potential error in the final sample, RMS error between the weights of the retained unique vehicle trips and the original sample of person trips was computed. The resulting magnitude of the error (estimated to be 16.4%) is misleadingly high because it is not appropriate to compare weights of unique vehicle trips with those of person trips that contained several duplicate vehicle trips. The final set of vehicle trip data was sorted to reconstruct travelday trip chains for each vehicle in the database. Each vehicle trip was marked with an end time. The soak period between two successive trips made by the vehicle was then computed based on the start time of the current trip and the end time of the previous trip. The NPTS travel-day data contained information only on a 24-h travel day, between 4:00 a.m. of one day and 3:59 a.m. of the next. Determining the soak times of the first trip made by each trip therefore was difficult. To facilitate the computation of first-start soak time, characteristics of the previous day’s travel activity are assumed to be identical to those of the travel day in terms of both person and vehicle trips. The trip chains used for final analysis were subjected to a series of consistency checks. A few trip chains with questionable or insufficient information were scrutinized closely. In providing NPTS data, respondents reveal the start time and duration of trip from a trip log or from memory. The soak period data analysis revealed several instances in which the computed soak periods appeared questionable or unreliable. For example, even though a meticulous data-screening process was undertaken to prevent the occurrences of overlapping person trip chains, a few vehicle trips had overlapping durations.
The U.S. Department of Transportation (DOT) is the primary sponsor of the NPTS, which is conducted periodically (usually every 5 years). The NPTS serves as the nation’s inventory of daily personal travel and is the only authoritative source of national data regarding daily trips and their relationships to the households, vehicles, drivers, passengers, and geographic characteristics of trip makers. These data are collected for all kinds of trips; various modes, purposes, and lengths; and all geographic areas of United States. The survey data, used primarily to enhance understanding of travel behavior, enable DOT officials to assess program initiatives, review programs and policies, and plan for the future. The NPTS is a tool in the urban transportation planning process; it provides data on personal travel behavior, trends in travel over time, trip generation rates, and national benchmarks (to use in reviewing local data) as well as other information for use in planning and modeling applications (13). Given the scientific and periodic nature of the NPTS, the travel data available in this well-designed and skillfully executed study offer significant promise to modelers of transportation emissions. The 1995 NPTS data include information on 409,025 trips made in 1 day (travel day) by more than 75,000 vehicles from more than 42,000 households across the United States (13). The file structure and content of the 1995 NPTS data are similar to those of the 1990 NPTS data. Venigalla et al. (3, 7 ) have successfully used the 1990 NPTS data files to derive aggregate trip activity parameters for inputs to the MOBILE5x models. These parameters were organized as a series of lookup tables stratified by geographic region, trip purpose, and time of day. The tables were developed specifically for use with the MOBILE5x models, and the results are of little value for use with the MOBILE6 model. However, the methodology used in deriving these parameters can be extended to derive the inputs for use with MOBILE versions 5x and 6. The two most relevant files for this study are those for vehicles and travel-day trips. However, all the data from these files are not necessary for computing the soak times. Start time and end time of each trip made using a household vehicle is enough determine the soak time. However, the analysis (which computes merely the engine soak period for each trip) is incomplete without some associated interpretation with other variables, such as: • Household identification number, • Vehicle identification number, • Make and year of the vehicle, • Geographic classifications [census region, census division, metropolitan statistical area (MSA), and size of MSA], • Start time and length of trip, and • Trip purpose variables. The above-mentioned variables were extracted from the 1995 NPTS database to create a smaller, more manageable database for the analysis. The data were then assessed for their suitability to derive soak times. This scrutiny consisted of four actions:
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Consequently, these trips in the chains showed negative soak periods. Whether to accept or reject the trips with negative soak periods was determined on the basis of the integrity of the vehicle trip chains that represented these trips. If other trips in the chain that contained a negative soak period did not overlap, soak periods with small negative soak values were set to zero. Otherwise, the trips (and in some cases, entire vehicle trip chains) were discarded from the analysis data. After the final screening process, the sampling characteristics of the retained unique vehicle trip data were assumed to be representative of the sampling characteristics of the 1995 NPTS database.
CHARACTERISTICS OF NPTS SOAK PERIODS Each of the soak times derived above was identified with the appropriate MOBILE6 soak interval category shown in Table 1. The frequencies of each of the 70 soak period intervals for each hour of weekday and weekend day were then obtained. Figure 1 illustrates the nationwide soak interval distribution for a weekday hour at 7 a.m. It can be seen that Soak Intervals 1 and 68 represent the highest concentration of starts. Most soak periods are concentrated in Intervals 13 and lower as well as 58 and higher. Soak Intervals 14 through 57, which account for soak times of 13 to 420 min, are sparsely represented in the distribution. Soak Interval 69 of the MOBILE6 model is designed to represent restarts with soak duration as low as one-hundredth of a minute. The NPTS survey methods adopted a travel time resolution of 1 min, which means that the NPTS data cannot be used to derive soak period distribution data for Soak Interval 69. The database included several trips of zero-soak periods, meaning that the vehicle was stalled. The reliability of these zero-soak data is questionable for reasons mentioned above. Even though MOBILE6 currently does not represent any stalled vehicles, Soak Interval 70 is reserved for representing trips with zero-soak periods in the future. For brevity, the trend plots of soak interval distribution for other time periods of a weekday or weekend day are not shown. However, they are similar to the distribution illustrated in Figure 1.
ANALYSIS OF VARIANCE OF AGGREGATE SOAK TIMES Nair et al. demonstrate the development and usefulness of disaggregate modeling methods developed for estimating soak period distributions (12). However, developing disaggregate models based on local data is a resource-intensive endeavor that many agencies cannot afford to undertake regularly. To assess the usefulness of aggre-
TABLE 1
MOBILE6 Soak Interval Classification of Soak Times (2)
Soak Interval Number, N
Range of Soak Time
1 2 to 30 31 to 45 46 to 67
Greater than 0.01 and ≤ 1.0 minutes Greater than (N-1) and ≤ N minutes Greater than (2N-32) and ≤ (2N-30) minutes Greater than (30N-1,320) and ≤ (30N-1,290) minutes
68 69 70
Greater than 720 minutes Greater than zero, but less than 0.01 Zero minutes (stalls, not used)
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gate soak period distributions derived from the NPTS data, a series of statistical analyses—mainly in the form of analysis of variance (ANOVA) and pairwise comparison of means of the main effects in the model tested for the ANOVA—was performed on the transferability of NPTS soak times. The response variable for the analysis was trip soak time, and the independent variables tested included time of day, trip purpose, and geographic variables (census region, census division, MSA size, and individual MSA). The results of this analysis are summarized in Table 2. The following observations were recorded based on the values presented in Table 2: • The variation in soak times is best explained by the trip purpose variable and is closely followed by the time (hour) of day variable. • Among geographic levels of aggregation, the census region, census division, MSA size, and individual MSAs have a statistically significant effect on soak times. In relative terms, individual MSAs have more influence on the soak period distribution than the other three geographic categories, even though the difference is not pronounced. • Not all the interaction effects of the geographic variables are significant. For example, although the time of day is an important determinant of soak period, its effect does not consistently vary by geographic location or by trip purpose.
GEOGRAPHIC TRANSFERABILITY OF SOAK DISTRIBUTIONS The realization that the geographic variables (especially individual MSAs) have an affect on the soak period underscores the importance of using local data for deriving soak period distributions. However, the significance of the effects at higher levels of geographic aggregation (such as MSA size, census division, and census region, in that order) is a finding for which NPTS soak times offer some promise. In the event that local data are not available or the local data from NPTS database are inadequate, agencies may be able to choose the next level of aggregation to derive soak periods for their analysis. Although this solution does not offer a perfect alternative to the local data, it is an improvement over the adaptation of national defaults. To assess the impact of various levels of aggregation on estimated soak times, a comparison was drawn among these levels for soak time data in the Dallas–Fort Worth area; results are presented in Table 3. The average soak times derived from the local data are comparable to those derived from the NPTS data subset for the Dallas–Fort Worth MSA. As the size of geographic scale of aggregation increases, the difference in average soak times increases. The sample size available in the NPTS data for the Dallas–Fort Worth area is of concern. However, the sample resolution is better at higher levels of aggregation. Comparing average values and sample sizes in this manner does not necessarily determine the overall variation across various levels of aggregation. This comparison is made only to develop a feel for the effect of aggregation on the average soak times. A better approach for ascertaining the findings of this comparison is to conduct statistical hypothesis testing with the actual data, which is left as an exercise for further research on this issue.
TRIP PURPOSE AND SOAK TIMES Even though soak times vary with the trip purpose variable, it is important to determine the practicality of deriving soak times for various trip purposes. Several considerations determined whether these
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120
45 40
Trips with Soak Interval 100
Cumulative Trips
80
30 25
60 20 40
15
Cumulative Trips (%)
Trips with Indicated Soak Interval (%)
35
10 20 5
67
64
61
58
55
49
52
46
43
40
37
34
31
28
25
22
19
16
13
7
10
4
0
1
0
MOBILE6 Soak Interval (min) FIGURE 1
Soak distribution for 7 a.m., nationwide.
values were to be further divided by trip purpose variables. As such, providing soak distributions by hour of the day for weekday and weekend travel requires that soak times be derived in as many as 3,360 cells. By further dividing these cells to account for trip purpose variables would require a larger sample size for analysis. Even if agencies have unlimited resources to acquire all the necessary data, the cost and benefit of such an exercise must be addressed first.
TABLE 2
If all the vehicle and trip characteristics are equal except for the trip purpose variable, then the emissions models do not differentiate the emissions resulting from a home-based work trip to that of a nonhome-based trip. Even if there are enough data to derive soak distributions classified by trip purpose variables, other emissions model inputs such as VMT and VMT split by highway class should be made compatible with the levels of disaggregation in the soak period distri-
Summary ANOVA for Trip Soak Times
Geographic Variable Source of Variance
Census Region
Census Division
MSA Size
Individual MSA
Model
0.0001 (38.17)
0.0001 (27.38)
0.0001 (28.02)
0.0001 (32.86)
0.0001 (576.354) 0.0001 (218.83) 0.0031 (4.62)
0.0001 (576.94) 0.0001 (219.06) 0.0001 (5.19)
0.0001 (576.24) 0.0001 (217.59) 0.0001 (6.58)
0.0001 (576.43) 0.0001 (218.85) 0.0048 (3.36)
0.2012 (1.17)a
0.0001 (9.80)
0.0442 (1.92)
0.0001 (9.78)
0.0001 (9.79) 0.0428 (1.31)
0.0008 (1.47) 0.1060 (1.14)a
0.0001 (6.72) 0.4481 (1.01)a
0.0001 (1.81) 0.8837 (0.85)a
Main Effects TP HD GV Interaction Effects GV*TP TP*HD HD*GV
NOTES: • A separate ANOVA was conducted for each spatial variable. • The figures indicate the probability of Type I error for each of the effects. • The figures in the parentheses indicate the F value for the indicated source of variance. • Unless otherwise indicated, the effect is significant at α = 5 percent. a Indicates that the effect is not significant at α = 5 percent. MSA = Metropolitan Statistical Area TP = Trip Purpose HD = Hour of Day GV = Geographic Variable (Census Region, Census Division, etc.)
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TABLE 3
Soak Periods at Various Levels of Aggregation
Number of Trips in the Sample
38,631 70,213
Nationwide
South
Census Region -
Census Division -
1,253
West South Central
Dallas–FW MSA
18,231
Data from NPTS
Survey Data a
Dallas–FW Local
1995 NPTS Data
252,312
Soak Time (min) for First-Start Minimum
71
63
30
30
30
Maximum
1,339
1,433
1,439
1,439
1,439
Average
835
892
910
915
915
Soak Time (min) for All Starts
a
Minimum
1
0
0
0
0
Maximum
1,345
895
1,198
1,260
1,415
Average
161
120
113
115
118
Data are from Nair et al. (12)
butions. In such a case, the effort required to model a simple scenario will increase exponentially. For a routine emissions modeling exercise, it may not be necessary to consider the soak period variation that is attributable to trip purpose. However, research studies that involve studying strategies for emissions reduction aimed at specific trip purposes may benefit from separating soak periods by trip purpose variables.
hours are generally higher than those for afternoon and evening hours. The overall trend does not change across various census MSA sizes. These observations are not only intuitive but also consistent with the ANOVA findings discussed above. The modeling setup for MOBILE6 is structured to seek soak period inputs by hour of the day. The modeler has the option of providing separate soak distributions for each hour or the same distribution for all hours. The ANOVA discussed above determined that the time-ofday variable influences soak times significantly. Consolidating certain time periods as separate groups would help reduce the efforts needed to provide soak distribution inputs. Additionally, grouping time periods would enhance the utility of travel survey data for deriving soak times by requiring fewer cells for which soak period distributions have to be developed. Nair et al. have consolidated the soak period data in six time periods: Morning (midnight to 6:30 a.m.), AM Peak (6:30 to 9:00 a.m.), AM Off-Peak (9:00 a.m. to noon), PM Off-Peak (noon to 4:00 p.m.),
TIME-OF-DAY INFLUENCE ON SOAK DISTRIBUTIONS
12 10 8 6
Census Region
4 2
Hour of Day FIGURE 2
Average hourly soak times by census region.
22
20
18
16
14
12
10
8
6
4
2
0 0
Average Soak Time (hrs)
To visualize the trends in variation of soak times by hour of the day, average soak times for different census divisions and MSA sizes were plotted (Figures 2 and 3, respectively). The general trend indicates high average soak times during the morning periods, then relatively flat during the midday and evening hours. The soak times for night
Wes Sou t North th North Centra l east
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10 8 Not in MSA over 3 mil 4 1 mil -3m il 500k -1m 2 il 250k - 500 k Less 0 than 250k 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
6
MS AS ize
Average Soak Time (hrs)
12
Hour of Day
FIGURE 3
Average hourly soak times by MSA size (k = thousand, mil = million).
PM Peak (4:00 to 6:30 p.m.), and Evening (6:30 p.m. to midnight) (12). The definitions for AM and PM Peaks were based on the definitions used by the transportation department of the North Central Texas Council of Governments. The time periods for remaining blocks of time were based on grouping criteria that are entirely subjective. Although this grouping indicated a significant influence on soak times, there was no scientific basis for establishing these groups. To establish scientific criteria for grouping time-of-day variables in to blocks, it is important to first identify the pairs of time periods for which there is a statistically significant difference in soak time distribution. They can be identified by using pairwise means comparison methods while conducting an ANOVA of soak times with the timeof-day variable. A Tukey’s pairwise comparison test was conducted to identify time-of-day pairs for which there was no statistically significant difference in soak times. A 5% level of significance was used. The analysis indicated that the soak times may be derived for the following periods of the day: • Night (11 p.m. to 4 a.m.), • Early morning (5 to 6 a.m.), • Afternoon (1 to 3 p.m.), • Evening (7 to 10 p.m.), and • Ten separate soak times for each hour between 7 a.m. and noon and between 4 and 7 p.m. The 14 time periods in this classification scheme reduces the total number of cells for which soak periods are to be derived from 1,680 to 980 (for a weekday or weekend). Most important, this consolidation enhances the travel data sample size in many cells, thereby enhancing the utility of the soak distributions derived from the travel data sample.
RECOMMENDED APPLICATIONS The study findings reinforce the prevailing general guidance on inputs to the modeling of emissions from mobile sources. That is, whenever possible, local data should be used to derive inputs to the emissions models. However, when local data are not available, agencies may
derive soak time distributions from NPTS data. Attempts should be made to develop soak time distributions for local conditions by using NPTS data for the subject area’s MSA group. If the NPTS data sample for the MSA of interest are inadequate, then as a next level of aggregation, soak times for the corresponding census division should be derived. The next geographic grouping in the hierarchy, census region, should be considered only when both MSA and census division data are found to be inadequate. National defaults may be used only as a last resort. Thus for soak period inputs to MOBILE6, the study offers alternatives to relatively accurate but more expensive local surveys as well as roughly aggregate and potentially inaccurate national defaults. The grouping scheme for consolidating soak distribution inputs by time period may be used in lieu of developing soak distributions for each hour in a 24-h period. The grouping scheme will enhance the utility of survey data in deriving the soak distributions and reduce the effort in providing soak distribution inputs to MOBILE6. The soak periods derived from NPTS data also may be used for deriving the operating-mode fractions inputs needed for MOBILE5B and modal emissions models such as CMEM. A helpful resource to emissions modelers will be a nationwide repository of travel activity data from which transferable input parameters can be borrowed. Agencies such as EPA and FHWA, which provide support to state and local agencies on various issues related to transportation and emissions modeling, may extend their roles by developing such a data repository. This support can start with the initial development of a database application for deriving soak distribution inputs based on NPTS data. Even though the MOBILE5x model will be phased out in early 2004 (2 years after full release of the MOBILE6 model), during the transition period, some agencies are allowed to use the older version. For this reason, the inputs repository should also support MOBILE5x for some time. REFERENCES 1. Chatterjee, A., T. L. Miller, J. W. Philpot, T. F. Wholley, Jr., R. Guensler, D. Hartgen, R. A. Margiotta, and P. R. Stopher. NCHRP Special Report 394: Improving Transportation Data for Mobile Source Emission Estimates. TRB, National Research Council, Washington, D.C., 1997.
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2. Draft User’s Guide to MOBILE6 Mobile Source Emissions Factor Model. Environmental Protection Agency, 1997 (revised 2002). 3. Venigalla, M., T. Miller, and A. Chatterjee. Start Modes of Trips for Mobile Source Emissions Modeling. In Transportation Research Record 1472, TRB, National Research Council, Washington, D.C., 1995, pp. 26–34. 4. An, F., M. Barth, G. Scora, and M. Ross. Modal-Based Intermediate Soak-Time Emissions Modeling. In Transportation Research Record: Journal of the Transportation Research Board, No. 1664, TRB, National Research Council, Washington, D.C., 1999, pp. 58–67. 5. Glover, E. L., and D. J. Brzezinski. Soak Length Activity Factors for Start Emissions. Report No. M6.FLT.003. Environmental Protection Agency, Office of Mobile Sources, Assessment and Modeling Division, 1998. 6. Glover, E. L., and P. Carey. Determination of Start Emissions as a Function of Mileage and Soak-Time for 1981–1993 Model Year LightDuty Vehicles. Report No. M6.STE.003. Environmental Protection Agency, Office of Mobile Sources, Assessment and Modeling Division, 1999. 7. Venigalla, M., T. Miller, and A. Chatterjee. Alternative Operating Mode Fractions to Federal Test Procedure Mode Mix for Mobile Source Emissions Modeling. In Transportation Research Record 1472, TRB, National Research Council, Washington, D.C., 1995, pp. 35–44.
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8. Venigalla, M. M., M. S. Bronzini, and A. Chatterjee. A Specialized Assignment Algorithm for Air Quality Modeling. Transportation Research, Part D, Jan. 1999. 9. Brodtmen, K. J., and T. A. Fuce. Determination of Hot- and Cold-Start Percentages in New Jersey. Report FHWA/NJ-84/001. New Jersey Department of Transportation, Trenton, July 1984. 10. Allen, W. G., and G. W. Davies. A New Method for Estimating Cold Start VMT. In Compendium of Technical Papers: 63rd Annual Meeting, The Hague, Netherlands. Institute of Transportation Engineers, Washington, D.C., 1993, pp. 224–229. 11. Benson, P. E. Corrections to Hot and Cold Start Vehicle Fractions for Microscale Air Quality Modeling. In Transportation Research Record 1176, TRB, National Research Council, Washington, D.C., 1988, pp. 87–92. 12. Nair, H. S., C. R. Bhat, and R. J. Kelly. Modeling Soak-Time Distribution of Trips for Mobile Source Emissions Forecasting: Techniques and Applications. Presented at the 80th Annual Meeting of the Transportation Research Board, Washington, D.C., Jan. 2000. 13. 1995 NPTS User’s Guide for the Public Use Data Files: Nationwide Personal Transportation Survey. Report No. FHWA-PL-98-002. FHWA, U.S. Department of Transportation, 1997. Publication of this paper sponsored by Committee on Transportation and Air Quality.
