TRD v1.0

INTRODUCTION

The paper investigates the energy and environmental impacts of route choice decisions using in-field collected Global Positioning System (GPS) data and simulation results. Traffic congestion has grown significantly in the past two decades. A recent study found that the total hours of delay in the United States increased by 528% (0.7 to 3.7 billion hours) from 1982 to 2003, and individual travelers spend about three times as many extra delay hours (16 to 47 hours) than they did twenty years ago. Furthermore, congestion affects more roads, trips, and times of day in most U.S. metropolitan areas (Schrank and Lomax 2005).

Due to congestion, motorists face a difficult trip-planning process when attempting to reduce delays and improve travel time reliability. This decision-making process is based on the drivers’ urgency, experience, and current information on travel time, trip distance, and other trip-related factors. However, energy and environmental impacts are not typically utilized in drivers’ decision-making process.

Motorists typically choose routes that minimize their travel cost (e.g., travel time). Therefore, drivers typically select longer routes if it can save travel cost. However, the question that needs addressing is whether taking a longer but faster route can result in energy and air quality improvements. This study investigates the impacts of route choice decisions on vehicle fuel consumption and emission rates using GPS data gathered during the morning commute near a suburb in the Washington, DC metropolitan area. The study analysis is further expanded by conducting a sensitivity analysis using the INTEGRATION microscopic traffic simulation software.

The objectives of this study were twofold. First, the study investigates the impact of two route choices on vehicle fuel consumption and emission rates. Second, the study demonstrates the effectiveness of utilizing User-Equilibrium (UE) and System Optimum (SO) traffic assignments for environmental improvement and energy saving considerations.

OVERVIEW OF ENVIRONMENTAL IMPACTS OF TRAFFIC ASSIGNMENT

The commonly used UE and SO traffic assignment models typically utilize minimum travel time as a generalized cost to assign traffic flows over a network. However UE and SO assignments are estimated based on travel time, the fuel consumption and emissions of UE and SO conditions may not produce optimum energy and emission rates. Several researchers have investigated traffic assignment methods using environmental cost functions.

Tzeng and Chen (1993) developed the multi-objective traffic assignment method. They formulated multi-objective functions using nonlinear programming techniques and produced various solutions to emit low CO emissions. By utilizing the eigenvector weighting method with pair-wise comparison, the researchers estimated compromised solutions for the flow patterns. They applied the case study of metropolitan Taipei to evaluate the developed traffic assignment model, which utilized the simplified travel time function and CO emission module. The study utilized a fixed amount of CO emissions per link and the total emissions were summed up across all vehicles on a link (Tzeng and Chen 1993).

Rilett and Benedek (1994) and Benedek and Rilett (1998) investigated an equitable traffic assignment with environmental cost functions. They emphasized the impacts of CO emissions when UE and SO traffic assignments were applied to a sample network, a simple network from Ottawa, Ontario, Canada and a calibrated network from Edmonton, Alberta, Canada. The studies utilized a simple macroscopic CO emission model used in the TRANSYT 7F software. The emission model utilized the average speed and the link length as input variables. The researchers showed that the traffic flows of the SO-CO (the traffic flows that have the minimum total CO emissions) condition were roughly equivalent to the flows of the UE and SO conditions within a small error range (Benedek and Rilett 1998; Rilett and Benedek 1994).

Sugawara and Niemeier (2002) developed an emission-optimized traffic assignment model that used average speed CO emission factors developed by the California Air Resources Board (CARB). The sample network case study concluded that emission-optimized trip assignments can reduce system-level vehicle emissions moderately when compared to the time-dependent UE and SO conditions. The research also found that the emission-optimized assignment is most effective when the network is under low to moderately congested conditions, saving up to 30% of total CO emissions; when the network is highly congested, the emission reduction is diminished to 8%. The authors explain that under emission-optimized conditions, less traffic volume is assigned to the freeway because emission levels are very high at freeway free-flow speeds (Sugawara and Niemeier 2002).

Nagurney and her colleagues developed a multi-class and multi-criteria traffic network equilibrium model with an environmental criterion and claimed that a desired environmental quality standard can be achieved by the proposed model through a particular weighting method. In the study, a fixed amount of CO emission rate per traveler per link was utilized to estimate the total CO emissions (Nagurney 2000; Nagurney and Dong 2002; Nagurney et al. 1998).

As demonstrated, a number of researchers have focused on traffic assignment methods that can improve the environment. However, these research efforts have utilized simplified travel time functions and simplified mathematical expressions to compute emission rates based on average link speeds without regarding transient changes in a vehicle’s speed and acceleration as it travels. These approaches have been accepted by many researchers due to their simplicity; however, these methods are not adequate to quantify the energy and environmental impact of route choice behavior, particularly on congested networks, due to vehicle operational behaviors. To overcome the limitations of current research methods of evaluating the impact of traffic assignment, this study adopted a microscopic fuel consumption and emission models using instantaneous speed and acceleration as explanatory variables.

STUDY CORRIDOR CHARACTERISTICS

To identify the energy and environmental impacts of route choice behavior, morning commute GPS data were collected in the Northern Virginia area. As shown in Figure 1, the arterial route, VA Route 7, extends over 22.6 km (17.25 mi) and covers 32 signalized intersections. The study section started at the intersection of VA 28 (Sully Road) to the west and extended to the intersection of I–66 to the east. The corridor’s entire length is divided, with a four-lane cross-section on the eastern side and a six-lane cross-section on the western side. The posted speed limits range from 56 km/h (35 mph) on the congested east side to 88 km/h (55 mph) on the west side.

The highway route connects two highway sections and two arterial sections as shown in Figure 1. The highway section extends from the intersection of VA 28 (Sully Road) and Route 7 to the south and connects to a section of VA 267 (Dulles Toll Road) and a section of I–495 and finally connects to Route 7. The total distance of the highway route is 35.85 km (22.41 mi) of which 22.56 km (17.25 mi) traverses highways (VA 267 and I–495). The arterial section of this route consists of a section of VA 28 that extends over 9.94 km (6.21 mi) and covers four signalized intersections and a section of Route 7, which is 3.35 km (2.10 mi) long and has six signalized intersections.

Traffic flows along the corridors are typically directional. During the morning peak, traffic along the study corridors generally moves eastbound, toward downtown Washington, DC and Fairfax, Virginia. It should be noted that the eastern portion of the study section, which has closely-spaced signalized intersections on VA 7, is typically more congested than other portions of the study section. The study corridors are controlled by a centralized, computerized signal system with an optimized cycle length of 180 seconds or 210 seconds depending on the time of day. Most of the signal cycle time is assigned to the main route VA 7 and VA 28. The directional distribution of signal timing varies according to the time-of-day and, during the morning peak, more signal timing is assigned to the eastbound direction. These signal timings are continuously optimized by the Virginia Department of Transportation (VDOT) staff and thus represent the state-of-practice in optimal signal control. Since the study only investigates the impact of different route choices on vehicle fuel consumption and emission rates, a detailed description of the traffic signal operations on the study corridor is beyond the scope of the paper. The study utilizes GPS data collected under current traffic signal operations on the study sections during the morning commute period.

GPS DATA COLLECTION PROCEDURES

GPS technology is increasingly being used for transportation-related applications. The study utilized a portable Wide Area Augmentation System (WAAS)-enabled GPS receiver to gather second-by-second vehicle trajectories along the study sections. The WAAS-enabled GPS receiver provides longitude and latitude data to an accuracy of 2 m, altitude data to an accuracy of 3 m, and speed measurements to an accuracy of 0.1 m/s.

The study used a portable GPS unit, GD30L, manufactured by LAIPAC Technology, Inc. The GPS unit is designed to record date, time, vehicle longitude, vehicle latitude, vehicle speed, vehicle heading, and the number of tracking satellites. The system is completely configurable, and the user can change the setup of the DIP switches to select the recording interval from 1 second to 30 min as well as the data recording format. The logged GPS data are recorded in a removable MultiMedia Flash Memory Card (MMC), and the 32MB MMC easily holds 30 days of passenger car operational data at 1-second intervals. A flash memory card reader was used to transfer the GPS data to a PC. The device is operated as a stand-alone unit without the need for a PC or other equipment. Once the GD30L is powered up, the GPS unit collects the data automatically.

The GPS floating-car travel data were collected using a test vehicle on weekdays (Monday through Friday) between March and May of 2006. The trip route (highway or arterial) was randomly selected on the day of data collection. In order to record the aggregate characteristics of traffic flow, the probe vehicle maintained the average speed of the traffic stream. The travel data were recorded at a 1-second resolution and downloaded to a personal computer. The minimum sample size (N) was calculated to satisfy the 95% confidence limits (Z value 1.96) using the standard deviation (σ) value and travel time error (δ) (see Equation 1). The GPS data that were gathered exceeded the required minimum sample size. In total, 39 valid trips were recorded, of which 21 traveled on the highway route and 18 traveled on the arterial route, while 10 trips on the highway route and 11 trips on the arterial route were required to satisfy the minimum sample size considering a 95% confidence limit.

(1)

It should be noted that the morning and evening commute data were gathered. However, this study focuses on the morning commute only. Since the GPS data included the entire morning commute travel data, the portion of the trips that covered the study sections was extracted from the entire trip for analysis purposes using a MATLAB code that was developed for this purpose. The software automatically identified the first and last GPS points within the study corridor using the coordinates of the boundary study sections. Following the data reduction, a unique trip number was assigned to each trip.

ENERGY AND EMISSION MODELS

In order to estimate emission and fuel consumption using the second-by-second GPS probe vehicle data, the VT-Micro model, the Comprehensive Modal Emissions Model (CMEM), and the Environmental Protection Agency’s (EPA) MOBILE6 model were utilized. The following sections briefly describe each model.

MOBILE6 Model

The MOBILE6 model was developed by the EPA Office of Transportation and Air Quality. MOBILE6 is the latest of the MOBILE models. MOBILE6 was developed using recent vehicle-emission testing data collected by the EPA, CARB, and automobile manufacturers, as well as inspection and maintenance tests conducted in various states. A major characteristic of the MOBILE6 model is the addition of so-called off-cycle emissions, which involve aggressive driving with various facility-type modeling. MOBILE6 estimates emission factors based on different roadway types (e.g., highways, arterials, locals). Emission factors can be adjusted for different facility types and different average speeds based on vehicle testing over a series of facility cycles. Also, MOBILE6 estimates emission factors for the start portion and the running portion of the trip separately (Miller et al. 2000).

It should be noted that in order to model the highway trips, the average speed of each section (a highway section: VA 267 and I-495, and two arterial sections: VA 28 and VA 7) was individually simulated and combined later in the analysis. Since the study only demonstrates the relative energy and emission differences associated with motorists’ route choices, only average speeds by facility type were utilized for the sensitivity analysis. The study assumed identical conditions for other input variables for both routes. Thus, default settings of vehicle model year, mileage rate, vehicle age, vehicle-type percentage, and altitude information were used in the models instead of the fleet characteristics of Northern Virginia. Furthermore, only exhaust running emissions of light duty gasoline vehicles (LDGV) without start emissions were utilized for the study.

CMEM Model

The Comprehensive Modal Emissions Model was developed by researchers at the University of California, Riverside. CMEM estimates light-duty vehicle (LDV) and light-duty truck (LDT) emissions as a function of the vehicle’s operating mode. The term “comprehensive” is utilized to reflect the ability of the model to predict emissions for a wide variety of LDVs and LDTs in various operating states. For the test data, both engine-out and tailpipe emissions of over 300 vehicles, including more than 30 high emitters, were measured second-by-second over three driving cycles, including the Federal Test Procedure (FTP), US06, and the Modal Emission Cycle (MEC). CMEM predicts second-by-second tailpipe emissions and fuel-consumption rates for a wide range of vehicle and technology categories. Vehicle operational variables (such as speed, acceleration, and road grade) and model-calibrated parameters (such as cold-start coefficients and engine-friction factor) are utilized as input data (Barth et al. 2000). In order to estimate fuel consumption and emissions, the CMEM vehicle categories 11 and 24 were utilized. Category 11 represents Tier 1, relatively new low-mileage vehicles, and category 24 represents Tier 1, relatively old high-mileage vehicles.

VT Micro (ORNL) Model

While the CMEM model was developed as a power-demand model, the VT-Micro model was developed as a regression model from experimentation with numerous polynomial combinations of speed and acceleration levels to construct a dual-regime model of the form

(2)

where L^e_i,j and M^e_i,j represent model regression coefficients for measures of effectiveness (MOE) “e” at speed exponent “i” and acceleration exponent “j”. It should be noted that the intercept at zero speed and zero acceleration was estimated using the positive acceleration model and fixed in order to ensure a continuous function between the two regression regimes. Consequently, the calibration of the model involves estimating a total of 32 parameters for each MOE.

The model was developed utilizing a number of data sources including data collected at the Oak Ridge National Laboratory (ORNL) (9 vehicles) and the EPA (101 vehicles). These data included fuel consumption and emission rate measurements (CO, HC, and NOx) as a function of the vehicle’s instantaneous speed and acceleration levels. CO₂ emissions were estimated using the carbon balance equation in conjunction with the fuel consumption measurements. In this study, an average composite vehicle for the nine ORNL vehicles was utilized. This composite vehicle included six light-duty automobiles and three light duty trucks. These vehicles were selected in order to produce an average vehicle that was consistent with average vehicle sales in terms of engine displacement, vehicle curb weight, and vehicle type at the time the data were gathered. Specifically, the average engine size was 3.3 liters, the average number of cylinders was 5.8, and the average curb weight was 1497 kg (3300 lbs). The VT-Micro model fuel consumption and emission rates were found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99. Given that the model utilizes the vehicle's instantaneous speed and acceleration levels as independent variables, the model is easy to use for the evaluation of the environmental impacts of operational-level projects including Intelligent Transportation Systems (ITS). A more detailed description of the model derivation is provided in the literature (Melosh et al. 1990; Rakha et al. 2004b).

GPS DATA ANALYSIS

The average travel time results of the collected GPS data demonstrate that highway trips result in travel time savings of 17% with a smaller travel time variability compared to arterial trips (travel time standard deviation of 4.17 versus 5.08 min., respectively), as summarized in Table 1. In order to confirm the results, t-tests were performed at a 5% significance level assuming identical mean travel times for both cases. The t-test produced a p-value of 0.003, which indicates that there is sufficient evidence to reject the null hypothesis of equal travel times. Thus, we conclude that the travel times of the highway trips are significantly shorter than the travel times of the arterial trips even though the highway trips are 30% longer than the arterial trips (35.9 km versus 27.6 km). Table 1 also demonstrates that the highway trips have a significantly higher average speed (85.42 km/h, 53.08 mi/h) than the arterial trips (56.62 km/h, 35.18 mi/h).

The fuel consumption and emission rates estimated by the VT-Micro (ORNL), MOBILE6, and CMEM models for the highway and arterial trips are illustrated in Figure 2. It should be noted that the absolute values of fuel consumption and emissions are not necessarily equal given the potential for different model characteristics; however, the intent of this paper is not to derive definitive emission inventories but to demonstrate the relative energy and emission differences associated with motorists’ route choices. Indeed, since each emission model is based on a different dataset, each model generates different fuel consumption and emission values. It should be noted that the CO₂ and fuel consumption values of MOBILE6 are not presented in Figure 2 because MOBILE6 only estimates HC, CO, and NO_x emissions.

The figure demonstrates that a 47% reduction in HC emissions is achievable if motorists use the arterial route instead of the highway route based on the VT-Micro (ORNL) model estimates. Similarly, the output of the CMEM model demonstrates that the arterial trips result in savings in the range of 47 to 63% in HC emissions compared to the highway trips even though the highway trips can save 17% in travel time. Alternatively, the MOBILE6 model results demonstrate that highway trips reduce HC emissions by 36%.

Figure 2 also illustrates the route choice impacts on other MOEs, including CO, NO_x, CO₂, and fuel consumption. Specifically, reductions in CO emissions in the range of 52, 71, and 64% were observed when motorists utilized the arterial based on the VT-Micro (ORNL vehicle), CMEM 11, and CMEM 24 models, respectively. The figure also shows that the use of the arterial route can produce reductions in NO_x and CO₂ emissions up to 45 and 20%, respectively. It is interesting to note that 18 to 23% of energy cost can be saved when motorists sacrifice 4.3 minutes (17%) of travel time in this case study. However, it should be noted that similar to the results of HC emission, the MOBILE6 model estimated that CO and NO_x emissions were increased 7 and 23% when drivers selected the arterial route.

Figure 3 illustrates the variations in HC emissions over the individual trips with travel time and average speed data for both highway and arterial trips. The figure illustrates that the trend of HC emissions from VT-Micro (ORNL vehicle) models are consistent with that of the CMEM models. It should be noted that both models are microscopic emission models developed to capture the second-by-second operational behavior of individual vehicles. The figure also shows that the HC emissions of the MOBILE6 model were found to be more sensitive to the average speed of each trip than to the intricate differences in speed profiles. Because the CMEM, VT-Micro, and MOBILE6 models were developed based on different emission sources, the absolute values of each emission model are different. However, it should be noted that the general trends are consistent for the micro models but inconsistent in the case of the MOBILE6 model.

CO, NO_x, and CO₂ emissions and fuel consumption rates of individual trips are also illustrated in Figure 4. As shown in the figure, the trend of CO emissions in the VT-Micro model (ORNL vehicle) and CMEM models along highway and arterial trips is analogous to that of HC emissions. In case of CO emissions, highway trip 12 (24.97 min., 86.03 km/hr) produces significantly higher CO emission (3.14 times) compared to highway trip 13 (24.98 min., 86.05 km/hr) with CMEM 24 model, demonstrating that CO emissions are more sensitive to engine loads than other air pollutants. It should be noted that highway trip 12 produced 50 high engine load occurrences that each produced emissions of CO in excess of 5 g/s while highway trip 13 had only 8 instances of such cases.

It should also be noted that the CO and NO_X emissions of the MOBILE6 model for arterial trips were found to be less sensitive to differences across the drive cycles, thus demonstrating that the MOBILE6 model cannot reflect the instantaneous changes in speed profiles. Additionally, note that the trends of CO₂ and fuel consumption are identical for both highway and arterial trips.

Correlation coefficients among travel time, average speed, fuel consumption, and emissions of individual trips are shown in Table 2. The table shows that it is difficult to find relationships between travel time (or average speed) and the estimated emissions and fuel consumption rates produced by the VT‑Micro (ORNL) and CMEM models considering the non-linear behavior of vehicle emissions, demonstrating that MOEs produced by microscopic emission models are closely related to instantaneous vehicle operations as opposed to aggregate trip characteristics. However, high correlation coefficients are observed between travel time and HC and NO_X emissions of MOBILE6 in Table 2. Also, the emissions and fuel consumption rates of the VT-Micro (ORNL vehicle) model are highly correlated with the results of the CMEM model due to the microscopic nature of the models.

Under congested conditions, motorists tend to choose the route with the minimum travel cost (e.g., travel time). Between each origin and destination, UE is reached when no driver can unilaterally achieve a reduction in time or cost by changing his/her route of travel; in other words, the travel times for all routes are equal. Emissions and fuel consumption rates produced by the VT-Micro (ORNL vehicle), CMEM, and MOBILE6 models are presented in Figure 5 when the travel times of the highway trip (28.8 min) and the arterial trip (28.8 min) are identical. It may not be appropriate to claim that the UE condition is reached even under these conditions because the two trips were recorded on two different days. Figure 5 illustrates that even when the travel times are identical, motorists can save significant emission and fuel consumption rates when the arterial route is selected. Specifically, the VT-Micro model estimates reductions in the range of 44%, 49%, 25% and 18% for HC, CO, NO_X, and CO₂ emissions, respectively. Furthermore, a driver can save up to 19% in fuel costs by using the arterial route.

Second-by-second emissions and fuel consumption of highway and arterial trips under UE conditions are illustrated in Figure 6. The figure also includes the speed profiles of the highway and arterial trips, which involve several full and partial stops in addition to high-speed travel (speeds of about 100 km/h or 62 mph for the arterial trip and 120 km/h or 75 mph for the highway trip). The average speeds of the highway and arterial trips were 74 km/h (46 mph) and 57 km/h (35 mph), respectively. Figure 6 illustrates the variations in the instantaneous vehicle emissions and fuel consumption rates as estimated by the VT-Micro (ORNL vehicle) and CMEM 24 models as the vehicle traveled along the highway and arterial routes. The instantaneous emissions show the numerous peaks and valleys of the vehicle emissions, demonstrating that the MOEs are sensitive to changes in a vehicle’s speed and acceleration profile. Also, it is noted that some peaks of HC and CO emissions represent a significant amount of total emissions. For example, a few peaks of CO emissions are several thousand times greater than those of other instantaneous emission rates.

Table 3 shows that 1% (17 seconds of 1,729 seconds) of the highway trip is responsible for 16%, 19%, 4%, 3%, and 4% of the total HC, CO, NO_X, CO₂ emissions, and fuel consumption rates, respectively, along the drive cycle shown in Figure 6 when the MOEs are estimated by the VT‑Micro (ORNL) model. The CMEM 24 model shows similar results: 20%, 38%, 30%, 3%, and 5% of total HC, CO, NO_X, CO₂ emissions, and fuel consumption rates, respectively, are produced by the 1% portion of the trip. The results of the CMEM 24 model shows that almost 100% of HC and NO_X emissions are emitted by 10% (173 seconds of 1,729 seconds) of the arterial trip, implying that a small fraction of the trip is responsible for a significant amount of the total emissions while the results of the VT-Micro show smaller contributions. Note that, in general, CO₂ emission and fuel consumption rates are impacted less than other MOEs; up to 25 and 28% of total CO₂ emissions and fuel consumption rates are caused by 10% of the trip profile. These small portions of a trip are caused by high engine-load conditions. Consequently, the study demonstrates that a reduction of high-emitting driving behavior can significantly improve air quality.

SIMULATION RESULTS

In order to examine the system-wide impacts of traffic assignment on air quality and energy, the study utilized the INTEGRATION microscopic traffic assignment and simulation software to conduct further analyses. The INTEGRATION software utilizes VT-Micro fuel consumption and emission models with the ORNL vehicle type and a detailed description of the energy and emission modules within the INTEGRATION model is provided in the literature (Rakha and Ahn 2004).

The sample network consists of two links and two nodes, as illustrated in Figure 7. The O-D (origin-destination) demand is 5,000 vehicles per hour (veh/hr), and there are two routes available. The highway route is 5 km (3.1 mi) long with three lanes for the first 4 km (2.5 mi) and two lanes for the remaining 1 km (0.6 mi). It has a free-flow speed of 100 km/hr (62.5 mph) and a capacity of 1,800 veh/hr/lane. The jam density of the highway route is set to 100 veh/km, and the speed-at-capacity is coded at 90 km/h (56.3 mph). The arterial route is a shorter two-lane, 4-km (2.5‑mi) corridor that has three signalized intersections located every 1 km (0.6 mi), as illustrated in Figure 7. The arterial route has a lower free-flow speed of 75 km/hr (47 mph) with a 67 km/h (42 mph) speed-at-capacity and an identical jam density and lane capacity as those for the highway route. Also, it should be noted that three signals have a 60-second cycle length with a 0.67 g/C ratio (effective green time to cycle length ratio), and they are partially coordinated.

Eleven traffic assignment scenarios were utilized in this case study. Figure 8 shows the traffic assignment scenarios simulated using the INTEGRATION software. The vehicles assigned to the arterial route were increased from 500 vehicles (scenario 2) to 4,500 vehicles (scenario 10) in increments of 500 vehicles; for example, scenario 5 shows that 2,000 vehicles were assigned to the arterial route and 3,000 vehicles were assigned to the highway route. Scenarios 1 and 11 were excluded from the rule; in other words, 100 vehicles and 4,900 vehicles were assigned to the arterial route and the highway route, respectively, in scenario 1 and vice versa for scenario 11. Each simulation was loaded for 30 minutes and continued simulation for 90 minutes in order to clear all loaded vehicles. Therefore, 2,500 vehicles were utilized for this analysis.

The simulation results show that the SO condition is attained in scenario 5, as illustrated in Figure 8, which has the smallest total travel time of the entire network. Figure 8 also shows the UE condition in scenario 4, which has the same average travel time for the two routes. It should be noted that the total travel times (or total delay) are significantly increased as 4,000 veh/hr or more are assigned to the arterial route.

The emissions- and/or fuel-consumption-optimized traffic assignments are illustrated in Figure 9. Scenario 8 is the HC and CO emission-optimized traffic assignment while scenario 5, which is the SO condition based on travel time, also produces the minimum CO₂ emission and fuel consumption rates. These results are inconsistent with the results of Rilett and Benedek (1994), in which the traffic flows of the CO-optimized assignment condition were roughly equivalent to the flows of the UE and SO conditions within a small error range. Also, scenario 11, which assigns most of the vehicles to the arterial route and has the greatest total delay among all scenarios (8.4 times more delay than SO condition), produces the least NO_X emissions. It is concluded that because more NO_X emissions are produced under stoichiometric air/fuel ratio conditions, low-speed congested traffic conditions, which are typically under fuel-rich or fuel-lean conditions, do not increase NO_X emissions. It is interesting to note that HC and CO emissions, NO_X emissions, CO₂ emissions, and fuel consumption rates have different emission- and/or energy-optimized assignments, which are scenarios 8, 11, and 5, respectively. It should be noted that each emission group has different characteristics; HC and CO emissions are extremely sensitive to vehicle acceleration behaviors, and the highest emissions are produced under a fuel-rich condition while more NO_X emissions are generated under a stoichiometric ratio condition. Fuel consumption and CO₂ emissions are also sensitive to acceleration behaviors but not as sensitive as HC and CO emissions.

Figure 10 illustrates emission- and energy-optimized traffic assignments for high-emitting (HE4) and low-emitting (LDV3) vehicles as well as the ORNL vehicles. High emitters are motor vehicles that produce higher emissions than the average-emitting vehicle under normal driving conditions. It is known that a small fraction of high emitters contributes significantly to total mobile source emissions. Detailed description of high-emitting (HE4) and low-emitting (LDV3) vehicles is provided in the literature

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