3

Traffic Flow Characteristics

Highway capacity enhancement projects have long been viewed as providing emission reductions and energy efficiencies by contributing to freer-flowing traffic conditions. In this chapter, the current knowledge about the initial effects of highway capacity additions on emissions and energy use is presented. Gaps and uncertainties in current understanding are identified. Finally, a summary appraisal of the factors most likely to affect outcomes is given, and recommendations for improving the state of knowledge are made.

OVERVIEW OF EXPECTED IMPACTS

The shaded box in Figure 3-1 illustrates the fundamental relationships involved in analyzing the initial impacts of a highway capacity addition. The primary impacts are changes in traffic flow patterns on the facilities affected by the improvement. Expansion of highway capacity should reduce the probability of stop-and-start traffic, raise average ve-



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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use 3 Traffic Flow Characteristics Highway capacity enhancement projects have long been viewed as providing emission reductions and energy efficiencies by contributing to freer-flowing traffic conditions. In this chapter, the current knowledge about the initial effects of highway capacity additions on emissions and energy use is presented. Gaps and uncertainties in current understanding are identified. Finally, a summary appraisal of the factors most likely to affect outcomes is given, and recommendations for improving the state of knowledge are made. OVERVIEW OF EXPECTED IMPACTS The shaded box in Figure 3-1 illustrates the fundamental relationships involved in analyzing the initial impacts of a highway capacity addition. The primary impacts are changes in traffic flow patterns on the facilities affected by the improvement. Expansion of highway capacity should reduce the probability of stop-and-start traffic, raise average ve-

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use FIGURE 3-1 Initial impacts of highway capacity additions and effects on air quality and energy use.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use hicle speeds, and reduce speed variability (i.e., smooth the traffic flow). All else being equal, stop-and-start traffic, low speeds, and highly variable speeds are all associated with high emission levels and poor fuel economy. Expansion of highway capacity should also reduce traffic density1 and improve levels of service (LOS) on the affected facilities. As capacity is added and congestion is eased on one link in the system, the effects will spill over onto other routes. Travelers who divert from other routes or shift their time of travel to take advantage of the new capacity will affect traffic flow patterns and traffic volumes on the broader network of highways (Figure 3-1), making the task of assessing the net effects on emissions and energy use more complex. Highway capacity additions can also affect other modes of travel. New capacity that reduces highway congestion and commuting time may encourage mass transit riders to switch back to their cars. Bicycle and pedestrian travel may be discouraged if the capacity addition (e.g., intersection widening) improves traffic flow patterns for automobiles and trucks to the detriment of slower-moving modes. The effects on travel demand of all these network changes—route shifts, shifts in time of travel, and mode shifts—are considered in Chapter 4; this chapter is focused primarily on the effects of these changes on the physical traffic flow. For the purpose of analyzing the initial impacts of highway capacity additions, travel demand is assumed to remain constant. Any additional traffic volume on expanded highway links is presumed to be a reallocation of existing traffic. Determining the net energy and emissions effects of changes in traffic flow patterns from a highway capacity addition also requires taking into account the construction phase of the project. Construction of certain capacity projects may cause traffic delays and create stop-and-start traffic conditions conducive to high emission levels. Finally, determining net effects depends on the consequences if the project is not undertaken: whether congestion will worsen if the new capacity is not added, whether there is adequate capacity in the system to avert congestion if travelers shift their travel routes and times of travel, or whether investments in other modes (e.g., transit and bicycle facilities) can accommodate travel needs. These outcomes must be compared with those of the “build” option to determine net effects.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use REVIEW OF EFFECTS ON EMISSIONS Effects of Changes in Traffic Flow Patterns Drive Cycles and Average Speed A motor vehicle trip consists of a beginning and an end connected by a series of accelerations, decelerations, constant speed cruising, and idling. To measure emissions and fuel consumption, trips are simulated on laboratory dynamometers and motor vehicles are tested using standardized drive cycles. Each drive cycle is composed of a unique profile of stops, starts, constant speed cruises, accelerations, and decelerations and is characterized by an overall average speed (Guensler 1994, 24). All vehicle trips at the same average speed are assumed to have the same underlying drive cycle regardless of facility type, roadway conditions (e.g., grade), or driver behavior. Different drive cycles are used to represent driving under different traffic conditions (Guensler and Geraghty 1991, 5–6). For example, the New York City cycle with an average trip speed of 11.4 kph (7.1 mph) reflects city driving at very heavy levels of congestion; vehicle speeds are low and vehicle movements consist primarily of stops, starts, and accelerations (Figure 3-2). The Highway Fuel Economy Test cycle with an average speed of 77.3 kph (48.3 mph) was originally created to reflect rural highway driving. Vehicle speeds are higher and there is more cruise-speed driving, although accelerations are still evident (Figure 3-2). As discussed in the following sections, however, current drive cycles do not adequately represent real-world driving conditions. Estimates from Current Emission Models Current estimates of emission rates embodied in the Environmental Protection Agency's (EPA's) MOBILE model and the California Air Resources Board's (CARB's) EMFAC model are expressed as functions of average speeds and are based on vehicle testing on a limited number of drive cycles. Baseline emission rates for light-duty motor vehicles are derived from the Federal Test Procedure (FTP) cycle. Emission rates at other average speeds are calculated by testing automobiles on 11 other drive

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use FIGURE 3-2 Drive cycles illustrating low-speed and high-speed driving profiles (Guensler 1994, Appendix A). Note: 1 mph = 1.6 kph.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use cycles and heavy-duty trucks on 4 drive cycles, including a baseline composite cycle (Table 3-1); adjusting baseline rates with the appropriate speed correction factors; and applying them to the appropriate vehicle class (see accompanying text box).2 The resulting average trip speed–emission curves (Figure 2-4, Figure 2-5, Figure 2-6, Figure 2-7, Figure 2-8 to Figure 2-9) can be used to give a preliminary estimate of a highway capacity addition's effect on emissions by comparing the distribution of traffic speeds on the affected highway links before and after the project (Figure 3-1). These curves can also be used to estimate predicted emission levels for the affected highway links both with and without the project. The comparisons are not exact, however, because average trip speeds are not equivalent to link-specific speeds for portions of vehicle trips. According to current models, if traffic is heavily congested and contributes to low average trip speeds [i.e., below 32 kph (20 mph)], capacity enhancements should initially reduce emissions of carbon monoxide (CO) and volatile organic compounds (VOCs) for automobiles and emissions of CO, VOC, and oxides of nitrogen (NOx) for heavy-duty diesel vehicles (HDDVs). Reductions in NOx emissions are likely to be small for automobiles; these emissions increase with speed at low average trip speeds [i.e., above 32 kph (20 mph) according to the MOBILE model and above 56 kph (35 mph) according to the EMFAC model]. If traffic is moderately to lightly congested and average trip speeds following a capacity addition exceed about 80 kph (50 mph), emissions initially should increase for all pollutants for automobiles and for NOx for HDDVs. Emissions of CO and VOCs should remain relatively flat at high speeds for HDDVs. If congestion is moderate and initial and final average trip speeds are in the intermediate range where the curves flatten out [i.e., between 32 and 80 kph (20 and 50 mph)], then capacity enhancement projects should only result in small emissions changes for both automobiles and HDDVs; emissions of CO and VOCs should be slightly reduced but partially offset by rising emissions of NOx. Modeled emissions estimates for particulates as a function of speed are unavailable, but industry data suggest that diesel particulate exhaust emissions follow the same trend as VOC emissions (i.e., they decline) up to about 80 kph (50 mph). Particulate exhaust emission levels at higher speeds are not well understood (Appendix A).

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use TABLE 3-1 Speed Correction Factor Emission Testing Cycles (Guensler 1994, 24, 25, 31; Appendix A; Guensler et al. 1991, 40) CYCLE AVERAGE SPEED NO. OF VEHICLES TESTED MPH KPH Light-Duty Vehiclesa Low Speed 1 2.5 4.0 236 Low Speed 2 3.6 5.8 236 Low Speed 3 4.0 6.4 236 New York City Cycle 7.1 11.4 464 Speed Cycle 12 12.1 19.4 464 Federal Test Procedure MOBILE 19.6 31.4 533 EMFAC 16.0 25.6 533 Speed Cycle 36 35.9 57.4 489 Highway Fuel Economy Test 48.3 77.3 533 High Speed 1 45.1 72.2 25 High Speed 2 51.0 81.6 25 High Speed 3 57.8 92.5 69 High Speed 4 64.4 103.0 69 Heavy-Duty Diesel Trucks New York Nonfreeway 7.3 11.7 22c Los Angeles Nonfreeway 16.8 26.9 22 Compositeb 18.8 30.1 22 Los Angeles Freeway 46.9 75.0 22 aAll of the testing cycles are conducted with vehicles in the hot stabilized mode with the exception of the FTP for the MOBILE model, which includes the weighted emission contributions from hot and cold start operations. bThe composite, baseline cycle is composed of the Los Angeles Freeway cycle and three nonfreeway cycles—the Los Angeles Nonfreeway and the New York Nonfreeway repeated twice. cThirty diesel engines were tested, but results from only 22 were used (9 medium heavy-duty trucks and 13 heavy heavy-duty trucks) to develop emission rate estimates.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use CALCULATION OF VEHICLE EMISSION RATES AND DERIVATION OF SPEED CORRECTION FACTORS Baseline emission rates were derived by driving thousands of new and in-use light-duty motor vehicles through the Federal Test Procedure (FTP), an emission test composed of a defined cycle of starts, stops, accelerations, and constant-speed cruises conducted on laboratory dynamometers (computerized treadmills). Emissions are collected in bags (part in the start mode, both cold and hot—Bags 1 and 3, respectively —and part in the hot stabilized mode—Bag 2). For the EPA MOBILE model, the emissions from vehicles operating in all three phases are used to calculate baseline emissions. The baseline emission rate (calculated in grams per mile) for a vehicle class is the averaged result from the three phases of the FTP for that vehicle class operating at an average speed of 31.6 kph (19.6 mph), the average test speed of the entire FTP. For the California EMFAC model, the baseline emission rate is the average emission result for the vehicle class operating under Bag 2 of the FTP, the hot stabilized cycle with an average operating speed of 25.6 kph (16 mph) (Guensler et al. 1993, 3–4). To estimate the emission rate for any vehicle at an average operating speed other than that of the FTP, the baseline emission rate is multiplied by the appropriate speed correction factor (SCF) associated with the applicable vehicle class and the operating speed to be modeled. SCFs are derived statistically. Emission data are again gathered from vehicles operating in the hot stabilized mode (Bag 2) on a number of different drive cycles with different profiles of stops, starts, constant speed cruises, and accelerations; each cycle has a different overall average speed. The SCFs are derived by regressing the average cycle speed on the average emission rate for the cycle (grams per mile from the aggregate bag sample of emissions and cycle distance). Thus, speed-corrected emission rates used in emission models are related to the average cycle speed and not to constant cruise speeds (Guensler and Geraghty 1991, 5–6). MOBILE5 baseline emission rates and SCFs for heavy-duty diesel trucks are based on tests using chassis dynamometers of 30 in-use heavy-duty diesel trucks (tests from 22 diesel engines were usable) on three drive cycles and a composite baseline cycle with the vehicles operating in the hot stabilized (Bag 2) mode (Appendix A). CARB has used the same SCFs in its EMFAC model, although new factors are under development (Appendix A).

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use Certainty of Emission Estimates from Current Models Unfortunately, estimates of the emission effects of changes in traffic flow patterns from a highway capacity addition are not nearly as precise as implied by the complex functions used to derive the speedcorrected emission rates. First, current model estimates are based on a limited set of drive cycles (Table 3-1) that inadequately represent specific traffic flow conditions. Many of the drive cycles on which emission estimates are based were developed decades ago (the FTP is more than 20 years old), when laboratory testing equipment capabilities were more limited.3 They are not believed to be representative of current real-world driving conditions (Effa and Larsen 1993, 1).4 For example, a comparison of three cycles with approximately the same average speed—the baseline FTP cycle and two cycles recently developed from driving on arterial roads and freeways in the Los Angeles area—shows the extent to which the baseline FTP drive cycle underestimates driving at higher speeds and accelerations, both of which are believed to be sources of high emissions (Figure 3-3 and Figure 3-4).5 Another problem is that little effort has been devoted to the development of driving cycles that represent high-speed operation. The only cycle developed from actual data on in-use operation is the Highway Fuel Economy Test, with an average speed of 77.3 kph (48.3 mph) (Figure 3-2, bottom). Because vehicles routinely travel at substantially higher average speeds on freeways, CARB developed a series of higherspeed cycles (Figure 3-5). Each uses the same portion of the Highway Fuel Economy Test cycle except that more time is allowed at the beginning and end for vehicles to accelerate to a higher initial cruising speed and to decelerate to a stop. The vehicles are required to perform the same accelerations at successively higher speeds, increasing engine power demands and the likelihood of higher emissions. The problem with this approach is that the cycles have not been validated with actual on-road data from customer service, and thus the resulting emissions estimates may be biased and may not represent real-world operation. A second issue related to the certainty of emission estimates is that current models predict the effects of changes in traffic flow characteristics on emissions solely on the basis of changes in average speed. This one-dimensional approach cannot adequately describe the un-

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use FIGURE 3-3 Speed-time traces of driving cycles with similar mean speeds (Effa and Larsen 1993, 18). Note: 1 mph = 1.6 kph.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use SUMMARY ASSESSMENT AND RECOMMENDATIONS FOR IMPROVING THE KNOWLEDGE BASE Adding highway capacity will not necessarily produce across-the-board reductions in emissions and fuel consumption, as generally has been believed. Although the current state of knowledge does not allow for precise estimates of the emissions and energy effects of traffic flow changes from capacity enhancement projects, a basic understanding of the major factors contributing to high emission levels and poor fuel economy for the current vehicle fleet suggests where benefits are most and least likely. Currently available information indicates that the highest levels of emissions and fuel consumption are associated with heavily congested traffic conditions, in which traffic is moving at low and highly variable speeds with frequent stops, idling, and accelerations. These conditions are prevalent on urban arterials with very low speeds and stop-and-start traffic. High levels of congestion are also found on some freeways, particularly at freeway-to-freeway interchanges. Capacity enhancement measures that result in significant smoothing of traffic flows and reductions in speed variability on these facilities, ideally at off-peak as well as peak-period travel times, should initially reduce emissions of all pollutants and conserve energy. The effects will be greater for emissions than for energy use, for CO and VOCs than for NOx, and for light-duty vehicles than for HDDVs. All else being equal, the more traffic volume affected by the improvement, including traffic on routes parallel to the improved facility, the greater the impact. Highway capacity additions under moderate or lightly congested conditions at which traffic speeds are less variable will have limited effects on smoothing traffic flows and may result in free-flowing traffic at freeway speeds that will increase energy use and emissions of some pollutants. Fuel use is known to increase rapidly at speeds above 56 to 72 kph (35 to 45 mph) for automobiles and above 80 kph (50 mph) for HDDVs. NOx emissions for automobiles, and NOx and VOC emissions for HDDVs, will increase although there is considerable uncertainty about the speed at which these increases begin and the magnitude of the increase for light-duty vehicles. Knowledge about

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use the behavior of CO and VOC emissions at free-flow freeway speeds is uncertain. For a broad range of intermediate traffic conditions, highway capacity additions are likely to have modest effects on traffic smoothing and to result in small upward shifts in the distribution of traffic speeds. The effects of such relatively small changes on traffic flow characteristics and related emissions and energy use cannot be predicted with confidence by current models. Evaluating the net effects of highway capacity expansion projects also requires taking into account any negative effects on emissions and energy use from traffic disruptions during project construction. Finally, it requires predicting changes in traffic flow characteristics and related effects on emissions and energy use had the project not been undertaken. Understanding how highway capacity additions are likely to change emission levels is only one step in determining how the projects ultimately will affect air quality. CO concentrations, which tend to be localized in a region and exacerbated by congested traffic conditions, are likely to be reduced by capacity enhancement projects that eliminate bottlenecks and smooth traffic flows on major highway links. The effects on ozone are likely to be more problematic. Emissions of NOx, an ozone precursor, will increase as a result of highway capacity projects that produce free-flow traffic conditions, particularly at freeway speeds. This could pose serious problems in ozone non-attainment areas where conformity requirements mandate reductions in NOx emissions. Although VOCs, the other major ozone precursor, would be reduced by traffic flow smoothing, other major sources of VOC emissions (i.e., cold starts and evaporative emissions) would remain largely unaffected by capacity enhancement projects. The ability to predict effects, particularly emission reductions for light-duty, gasoline-powered automobiles, is severely limited. Current emission models rely on average trip speed as the sole descriptor of traffic flow. Variability in speed, road grade, and other factors that strongly influence emissions are not explicitly dealt with. In addition, virtually all emissions testing has been based on a limited set of test cycles with questionable representativeness for specific traffic

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use flow conditions. Finally, model estimates of predicted changes in emission levels tend to have large variances for a wide range of changes in average trip speeds that are typical of many highway capacity additions. Diesel emissions can be predicted with greater certainty. They can be measured more directly (no aftertreatment is involved yet), and diesel engines require very little cold start or acceleration enrichment, which are large sources of variance for light-duty vehicle emissions. However, detailed data on diesel particulate emissions as a function of speed are not available. This is a troubling omission, because HDDVs are the primary source of highway vehicle particulate exhaust emissions, and particulate concentrations pose a significant health risk. Fuel economy estimates from simulation models are relatively reliable and valid indicators of actual in-use vehicle fuel consumption. In part, this reflects the fact that fuel economy is not as sensitive as emissions to changes in traffic flow conditions, particularly speed variation. Research has begun that could significantly improve understanding of the emissions characteristics of motor vehicles under real-world driving conditions. In response to a requirement of the CAAA, EPA has collected data on driving behavior in selected cities to develop new drive cycles more representative of current driving conditions. CARB has conducted parallel surveys in Los Angeles, has developed new drive cycles, and is testing them on a representative sample of 125 light-duty vehicles. The states have pooled resources to develop and verify a modal emissions model. However, federal leadership and funding are required to expand and accelerate these efforts. The most pressing needs are for the development of an emission rate model sensitive to a wider variety of driving patterns than current models and truly representative of the range of vehicles on the road today and for the collection of vehicle activity data to use as inputs to this model. EPA should become an active participant in developing these models and incorporating them into the regulatory process. Because the greatest source of variance in estimates of fuel economy is differences in vehicle design and technology, estimates of the

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use in-use fuel economy of the vehicle fleet should be periodically updated. Data and models for representing the effects of speed and acceleration on motor vehicle fuel economy should also be updated from time to time to be representative of the current vehicle fleet. This could be accomplished as part of the vehicle testing described above. Traffic simulation models should be modified to provide baseline data on travel speed and speed variation for individual vehicles, in addition to the more traditional average speed data for all vehicles that are currently reported. Differentiation should be made between automobiles and trucks. Such disaggregated data are needed to provide better estimates of emission levels. Current modeled estimates of vehicle velocity and acceleration rates should be validated if they are used to provide inputs to emission models. Traffic simulation models should also be integrated with regional forecasting models so that the long-term impacts of highway capacity additions on travel demand and emissions can be estimated. Finally, as is typical of many interdisciplinary efforts, lack of a common terminology hampers development of appropriate models. The terminology and definitions used by traffic engineers for key traffic flow parameters (e.g., delay) are not always consistent with terms used by air quality analysts. A strong effort is needed to remove such inconsistencies from research projects and bring traffic and air quality into consistent reference frames. Additional research and vehicle testing should help reduce the uncertainties associated with modeling emissions and energy use. However, it will take substantial time and investment for the results of this knowledge to be implemented in practical models. Although the models can be improved, they still cannot predict outcomes with absolute certainty. Good policy should allow for some variance in modeled results. In this chapter the current knowledge of the initial effects of highway capacity additions on emissions and energy use has been identified. If capacity enhancement projects stimulate new travel over time or encourage relocation of residences or businesses at more dispersed, automobile-dependent locations, the initial gains from some projects will be eroded. The potential for these outcomes is explored in the following chapters.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use NOTES 1. Density is defined as the number of vehicles occupying a given length of a lane or roadway, averaged over time. It is generally expressed as vehicles per mile (TRB 1992, 1–6). See discussion of level of service in Chapter 1 of this report. 2. EPA has not developed separate emission factors or speed correction factors for diesel buses. Available data suggest that bus emissions are significantly higher than truck emissions (Appendix A). 3. The old belt-driven chassis dynamometers could not handle acceleration or deceleration rates that exceeded 5.3 kph/sec (3.3 mph/sec) (EPA 1993, 13). 4. Another flaw of the drive cycle tests is that not all vehicles were tested at all speeds. 5. CARB is in process of testing 125 vehicles representative of the current fleet (reduced from an initial testing program of 250 vehicles because of budgetary constraints), to determine emission levels for these and several other cycles (personal communication, L. Larsen, CARB, April 14, 1995). 6. Confidence intervals measure the likelihood, frequently at a 95 percent level of confidence, that the predicted mean for a random sample will lie within the confidence interval bounds. A large confidence interval indicates a wide range of variability around the mean. 7. Guensler uses the same vehicle test data base that CARB used in developing the speed correction factors in the EMFAC model. He reran the regressions and developed confidence intervals using disaggregated test results. By contrast, the data aggregation techniques used by CARB did not retain the data variability necessary to establish confidence intervals. Guensler is undertaking the same kind of analysis for the MOBILE model, but the results are not yet available. 8. Transient and cold start effects account for 10 percent or less of VOC and NOx emissions for heavy-duty diesel trucks (Duleep 1994, 16). Thus, it is possible to develop emission estimates for almost any arbitrary driving cycle with reasonable accuracy from a steady state emissions map of the engine (Appendix A). 9. The actual level of the average speed depends on the free-flow speed of the facility, that is, the speed of a passenger car traveling at low traffic volume conditions (LOS A).

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use 10. The emissions and fuel consumption data were developed by the Oak Ridge National Laboratory for the Federal Highway Administration in the mid-1980s (see discussion in Chapter 2). These data are currently being updated. 11. Two methodological approaches were used. The first used instrumented vehicles recruited from inspection and maintenance stations to collect second-by-second data on driving behavior. This approach allows for collecting data on the full vehicle trip but may be biased because of the driver's knowledge of the instrumentation. The second approach involved chase cars, which followed randomly selected target vehicles. The approach is nonintrusive but does not allow data to be collected for complete trips (EPA 1993, 36, 43). 12. Decelerations, another characteristic of stop-and-start driving, were not found by EPA to increase emissions significantly. However, testing was performed on current technology (multipoint fuel injection) vehicles. With older carbureted technology, some increase in VOC emissions could occur (personal communication, John German, EPA, Feb. 23, 1994). 13. On arterial roads, 18.7 percent of the driving was at acceleration rates greater than 4.8 kph (3 mph) per second, versus 2.3 percent on freeways. Twenty-eight percent of the driving was at cruise-type conditions on arterials versus 53 percent on freeways. The freeway driving, however, did not include driving on ramps to enter the freeway (Effa and Larsen 1993, 3). 14. Although these events are less common, sharp accelerations at high speeds [i.e., above 80 kph (50 mph)] contribute a disproportionate share of total CO emissions (LeBlanc et al. 1994, Figure 3b). Duration of accelerations also appears to matter; CO emissions increase rapidly as the duration of a severe acceleration event increases (LeBlanc et al. 1994, 10). 15. Traffic-calming techniques fall into two general categories: traffic management strategies (e.g., signalization system improvements, transportation system and parking management, truck restrictions, and speed limits and enforcement) and physical design (e.g., narrowing and curving roadways, speed bumps, and pavement design) (Project for Public Spaces 1993, 15). 16. Emissions were estimated by driving instrumented cars both aggressively and calmly through the traffic-calmed area. These estimates are subject to the same limitations as is instrumented vehicle drive cycle research in the United States: potential bias because the driver is aware of the instrumentation and representativeness of individual driving patterns of

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use the entire traffic stream (personal communication, Carmen Hass-Klau, consultant, June 3, 1994. Hass-Klau believes that if traffic calming is introduced in a widespread way, driving behavior will be affected). 17. Schemes designed to encourage steady driving speeds are believed to be more effective in reducing emissions than those designed to encourage slow speeds per se. A comprehensive evaluation of traffic-calming projects (Pharoah and Russell 1989, 48–49) found that schemes that have resulted in very low speeds increase emissions of CO and VOCs because they caused more acceleration and deceleration and greater use of second gear. However, there was generally no net increase in air pollution because of the overall reduction in traffic volume. 18. Driving cycles more representative of current driving patterns are being developed. Sierra Research, for example, under contract to EPA, has developed three cycles in addition to the FTP to represent the range of in-use vehicle operation: (a) a start cycle that represents driving that occurs during the first 4 min after the start of the vehicle, (b) a non-FTP cycle that represents the distribution of speeds and accelerations outside the boundary of the FTP, and (c) a remnant cycle that represents everything else (Enns et al. 1993, 3, 4). CARB has developed seven freeway cycles and three arterial cycles, which are discussed in the text. 19. EPA may add an option to account for some “off-cycle” events (e.g., high speeds and high accelerations) in its next revision of the MOBILE model (person communication, Dave Brzezinski, EPA, Aug. 17, 1994). In fact, the agency has introduced a proposed rulemaking to add a supplementary cycle to the FTP, which would capture some of these off-cycle events (Federal Register 1995). 20. The work is being performed under contract to the Georgia Institute of Technology. No extensive vehicle testing is planned, however (personal communication, Carl T. Ripberger, EPA, Aug. 19, 1994). 21. Modal models predict second-by-second emissions for a wide range of engine speeds and loads under steady-state operations. The problem in predicting emissions at a particular engine speed and load is that the results are affected by vehicle operation in the period preceding the desired predicted value. For example, a modal model could predict emissions for a vehicle operating at a speed of 40 kph (25 mph) and an acceleration rate of 4.8 kph/sec (3 mph/sec). However, predicted emissions at 40 kph (25 mph) would differ depending on whether the vehicle has accelerated from 32 to 40 kph (20 to 25 mph), whether it is operating at a steady-state speed of 40 kph (25 mph), or whether it has decelerated from

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use 48 to 40 kph (30 to 25 mph). The challenge is to capture these transient operations. An alternative approach—an “event-based” modal model—could partially address these concerns. In an event-based model, modal emissions are predicted for a complete driving event representative of actual driving conditions, such as a 15-sec acceleration from 0 to 80 kph (0 to 50 mph). This approach captures the variability in emissions introduced by prior operations. 22. The Volpe National Transportation Systems Center has developed a PC-based model framework to estimate the relative impacts on emissions (and fuel economy) of intelligent transportation system user services. The models include a regional planning model (SYSTEM II), two traffic simulation models (FREQ, a freeway/ramp model, and TRANSYT-7F, an arterial model), and VEHSIME, a modal model that produces second-by-second estimates of emissions and fuel consumption under hot stabilized vehicle operation (Sierra Research 1994). 23. Researchers at the University of California, Riverside, are also developing a modal emissions model that can be integrated with traffic simulation models to more accurately portray the emissions effects of dynamic vehicle activities (e.g., accelerations and decelerations) on traffic networks. Although the researchers are using data from their own instrumented vehicle, they are depending primarily on outside sources for vehicle modal emissions data (CE-CERT 1993, 12). 24. However, morning congestion may be more important for the ozone precursors, because the pollutants have more time to be exposed to sunlight, which drives the chemical reactions that form ozone (Horowitz 1982, 70). 25. In 1993, approximately 90 percent of the fleet was equipped with the newer multipoint injection technology (personal communication, John German, EPA, Dec. 13, 1993). The technology enables each engine cylinder to operate with a more consistent air-fuel ratio. 26. Cold starts are not a large problem for diesel-powered heavy-duty trucks. VOC and particulate emissions are about 10 percent higher in the cold start mode (Duleep 1994, 12). 27. The Navistar model is well documented; its representatives claim that the model has been validated to within ±5 percent in real-world tests. The model has three built-in cycles to represent city, suburban, and highway driving conditions with average real-world tests. The model has three built-in cycles to represent city, suburban, and highway driving conditions with average speeds of 32, 64, and 88 kph (20, 40, and 55 mph), respectively (Appendix A).

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use 28. The range between the highest and lowest estimates of fuel economy for the 12 light-duty, gasoline-powered test vehicles is as follows: 40 kph, 15.0 kpl; 56 kph, 12.8 kpl; 72 kph, 5.6 kpl; 88 kph, 6.1 kpl; 104 kph, 5.1 kpl; and 120 kph, 4.4 kpl (25 mph, 35.4 mpg; 35 mph, 30.1 mpg; 45 mph, 13.1 mpg; 55 mph, 14.4 mpg; 65 mph, 12.1 mpg; and 75 mph, 10.4 mpg). 29. An et al. (1993) found that three variables—average speed, free-flow speed or maximum attempted speed when there is no congestion, and the percentage of total trip time the vehicle is stopped—explained more than 90 percent of the variations from using different drive cycles (p. 3). Diesel engines do not require acceleration enrichment, and the air-fuel ratio during a transient acceleration/deceleration is more carefully controlled than in a gasoline engine (Appendix A). REFERENCES ABBREVIATIONS CARB California Air Resources Board CE-CERT College of Engineering–Center for Environmental Research and Technology EPA Environmental Protection Agency NCHRP National Cooperative Highway Research Program NRC National Research Council TRB Transportation Research Board An, F., and M. Ross. 1993. Model of Fuel Economy and Driving Patterns. Presented at the 72nd Annual Meeting of the Transportation Research Board, Washington, D.C. An, F., M. Ross, and A. Bando. 1993. How To Drive To Save Energy and Reduce Emissions in Your Daily Trip . RCG/Hagler, Bailly, Inc., Arlington, Va., and the University of Michigan, Ann Arbor, 10 pp. CE-CERT. 1993. The Development of an Integrated Transportation/Emissions Model to Predict Mobile Source Emission. South Coast Air Quality Management District Contract AB2766/C0004 . University of California, Riverside, May. Cicero-Fernández, P., and J.R. Long. 1993. Modal Acceleration Testing on Current Technology Vehicles. Presented at Conference on the Emission Inventory: Perception and Reality, Pasadena, Calif., Oct. 18–20. Davis, S.C., and P.S. Hu. 1991. Transportation Energy Data Book: Edition 11. ORNL-6649. Center for Transportation Analysis, Energy Division, Oak Ridge National Laboratory, Tenn., Jan.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use Duleep, K.G. 1994. Briefing on Heavy Duty Diesel Vehicle Emissions. Energy and Environmental Analysis, Inc., Arlington, Va. Effa, R.C., and L.C. Larsen. 1993. Development of Real-World Driving Cycles for Estimating Facility-Specific Emissions from Light-Duty Vehicles. Presented at Conference on the Emission Inventory: Perception and Reality, Pasadena, Calif., Oct. 18–20, 20 pp. Enns, P., J. German, and J. Markey. 1993. EPA's Survey of In-Use Driving Patterns: Implications for Mobile Source Emission Inventories. Office of Mobile Sources, Certification Division, U.S. Environmental Protection Agency, Ann Arbor, Mich. EPA. 1993. Federal Test Procedure Review Project: Preliminary Technical Report . Office of Air and Radiation, May, 161 pp. Federal Register. 1995. Proposed Regulations for Revisions to the Federal Test Procedure for Emissions from Motor Vehicles. Vol. 60, No. 25, Feb. 7, pp. 7404–7424. Greene, D.L. 1981. Estimated Speed-Fuel Consumption Relationships for a Large Sample of Cars. Energy, Vol. 6, pp. 441–446. Guensler, R., and A.B. Geraghty. 1991. A Transportation/Air Quality Research Agenda for the 1990s. Presented at the 84th Annual Meeting and Exhibition, Air and Waste Management Association, Vancouver, British Columbia, Canada, June 16–21, 32 pp. Guensler, R., D. Sperling, and P. Jovanis. 1991. Uncertainty in the Emission Inventory for Heavy-Duty Diesel-Powered Trucks. UCD-ITS-RR-91-02. Institute of Transportation Studies, University of California, Davis, June, 146 pp. Guensler, R., S. Washington, and D. Sperling. 1993. A Weighted Disaggregate Approach To Modeling Speed Correction Factors . Presented at the 72nd Annual Meeting of the Transportation Research Board, Washington, D.C., 44 pp. Guensler, R. 1994. Vehicle Emission Rates and Average Operating Speeds. Ph.D. dissertation. University of California, Davis. Horowitz, J.L. 1982. Air Quality Analysis for Urban Transportation Planning. The MIT Press, Cambridge, Mass., 387 pp. Kelly, N.A., and P.J. Groblicki. 1993. Real-World Emissions from a Modern Production Vehicle Driven in Los Angeles. Air and Waste, Vol. 43, Oct., pp. 1351–1357. LeBlanc, D., M.D. Meyer, F.M. Saunders, and J.A. Mulholland. 1994. Carbon Monoxide Emissions from Road Driving: Evidence of Emissions due to Power Enrichment. Presented at the 73rd Annual Meeting of the Transportation Research Board, Washington, D.C., 23 pp. McGill, R. 1985. Fuel Consumption and Emission Values for Traffic Models. FHWA/RD-85/053. Oak Ridge National Laboratory, Tenn., May, 90 pp.

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EXPANDING METROPOLITAN HIGHWAYS: Implications for Air Quality and Energy Use NCHRP. 1994. Research Problem Statement: Development of a Modal-Emissions Model . NCHRP Project 25-11. Transportation Research Board, Oct. 6, 4 pp. NRC. 1992. Automotive Fuel Economy: How Far Should We Go? National Academy Press, Washington, D.C., 259 pp. Pharoah, T., and J. Russell. 1989. Traffic Calming: Policy and Evaluations in Three European Countries . Occasional Paper 2/89. Department of Planning Housing and Development, South Bank Polytechnic , London, United Kingdom, 67 pp. Project for Public Spaces. 1993. The Effects of Environmental Design on the Amount and Type of Bicycling and Walking. National Bicycle and Walking Study. FHWA Case Study 20. FHWA-PD-93-037. Federal Highway Administration, U.S. Department of Transportation , April, 40 pp. Sierra Research, Inc. 1993. Evaluation of “MOBILE” Vehicle Emission Model. Report SR93-12-02. Sacramento, Calif., Dec. 7. Sierra Research, Inc. 1994. Development of an Emissions, Fuel Economy, and Drive Cycle Estimation Model for IVHS Benefits Assessment Framework. SR94-07-01. Sacramento, Calif., July 9. TRB. 1992. Special Report 209: Highway Capacity Manual. 2nd edition revised. National Research Council, Washington, D.C.