Use of subsampled traffic data to estimate roadway emissions, including conversion to MOBILE6 vehicle classifications

Air quality is degraded by many factors, among which the emissions from on-road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy-making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NOx), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NOx emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Analysis and Modeling of Air Quality at Street Intersections

Four roadway intersection air pollution models were compared to experimental data and evaluated. The models included the recently developed Texas Intersection Model (TEXIN), The Intersection Midblock Model (IMM), the program MICRO and the Indirect Source Guidelines. Data obtained by Texas A&M University at two intersections in Texas and by CALTRANS at an intersection in California were used for the evaluation. The TEXIN Model performed best in all comparisons to the data. The IMM was almost as accurate as TEXIN but required an order of magnitude more input information and computer time. MICRO and the Indirect Source Guidelines performed poorly.     

A Preliminary Study of Modeling the Air Pollution Effects fromTraffic Engineering Alternatives

Three separate mathematical models were combined to calculate the changes in carbon monoxide (CO) concentrations that might result from traffic engineering changes. The three models used were: (1) The Dynamic Highway Transportation model (DHTM) which relates traffic flow patterns to physical parameters and traffic signal characteristics of a network; (2) an emission model that predicts CO emissions from traffic flow parameters such as number of stops, idling time, etc; and (3) the APRAC-1A urban diffusion model which calculates CO concentrations from source distributions and meteorological factors. The composite model was applied to traffic in downtown Chicago for a specific set of meteorological conditions. Results are compared for two traffic signal control schemes. In those blocks where concentrations were highest, the model indicates a 20% reduction is possible through improved traffic signal controls. The model should be useful for testing other traffic control measures.     

Soil Suspension/Dispersion Modeling Methods for Estimating Health-Based Soil Cleanup Levels of Hexavalent Chromium at Chromite Ore Processing Residue Sites

Abstract

The primary health concern associated with exposures to chromite ore processing residue (COPR)-affected soils is inhalation of hexavalent chromium [Cr(VI)] particulates. Site-specific soil alternative remediation standards (ARSs) are set using soil suspension and dispersion models to be protective of the theoretical excess cancer risk associated with inhalation of soil suspended by vehicle traffic and wind. The purpose of this study was to update a previous model comparison study that identified the 1995 AP-42 particulate emission model for vehicle traffic over un-paved roads and the Fugitive Dust Model (FDM) as the most appropriate model combination for estimating site-specific ARSs. Because the AP-42 model has been revised, we have updated our past evaluation. Specifically, the 2006 AP-42 particulate emissions model; the Industrial Source Complex–Short Term model, version 3 (ISCST3); and the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) air dispersion models were evaluated, and the results were compared with those from the previously used modeling approaches. Two sites with and two sites without vehicle traffic were evaluated to determine if wind erosion is a significant source of emissions. For the two sites with vehicle traffic, both FDM and ISCST3 produced total suspended particulate (TSP) estimates that were, on average, within a factor of 2 of measured; whereas AERMOD produced estimates that were as much as 5-fold higher than measured. In general, the estimated TSP concentrations for FDM were higher than those for ISCST3. For airborne Cr(VI), the ISCST3 model produced estimates that were only 2- to 8-fold of the measured concentrations, and both FDM and AERMOD estimated airborne Cr(VI) concentrations that were approximately 4- to 14-fold higher than measured. Results using the 1995 AP-42 model were closer to measured than those from the 2006 AP-42 model. Wind erosion was an insignificant contributor to particulate emissions at COPR sites.     

Soil suspension/dispersion modeling methods for estimating health-based soil cleanup levels of hexavalent chromium at chromite ore processing residue sites

The primary health concern associated with exposures to chromite ore processing residue (COPR)-affected soils is inhalation of hexavalent chromium [Cr(VI)] particulates. Site-specific soil alternative remediation standards (ARSs) are set using soil suspension and dispersion models to be protective of the theoretical excess cancer risk associated with inhalation of soil suspended by vehicle traffic and wind. The purpose of this study was to update a previous model comparison study that identified the 1995 AP-42 particulate emission model for vehicle traffic over unpaved roads and the Fugitive Dust Model (FDM) as the most appropriate model combination for estimating site-specific ARSs. Because the AP-42 model has been revised, we have updated our past evaluation. Specifically, the 2006 AP-42 particulate emissions model; the Industrial Source Complex-Short Term model, version 3 (ISCST3); and the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) air dispersion models were evaluated, and the results were compared with those from the previously used modeling approaches. Two sites with and two sites without vehicle traffic were evaluated to determine if wind erosion is a significant source of emissions. For the two sites with vehicle traffic, both FDM and ISCST3 produced total suspended particulate (TSP) estimates that were, on average, within a factor of 2 of measured; whereas AERMOD produced estimates that were as much as 5-fold higher than measured. In general, the estimated TSP concentrations for FDM were higher than those for ISCST3. For airborne Cr(VI), the ISCST3 model produced estimates that were only 2- to 8-fold of the measured concentrations, and both FDM and AERMOD estimated airborne Cr(VI) concentrations that were approximately 4- to 14-fold higher than measured. Results using the 1995 AP-42 model were closer to measured than those from the 2006 AP-42 model. Wind erosion was an insignificant contributor to particulate emissions at COPR sites.     

Estimation of vehicular emissions by capturing traffic variations

Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.     

Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing

Most existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

Implications: Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.     

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .     

Quantification of highway vehicle emissions hot spots based upon on-board measurements

The purpose of this study is to demonstrate a methodology for quantification of high emissions hot spots along roadways based upon real-world, on-road vehicle emissions measurements. An emissions hot spot is defined as a fixed location along a corridor in which the peak emissions are statistically significantly greater by more than a factor of 2 than the average emissions for free-flow or near free-flow conditions on the corridor. A portable instrument was used to measure on-road tailpipe emissions of carbon monoxide, nitric oxide, hydrocarbons, and carbon dioxide on a second-by-second basis during actual driving. Measurements were made for seven vehicles deployed on two primary arterial corridors. The ratio of average emissions at hot spots to the average emissions observed during a trip was as high as 25 for carbon monoxide, 5 for nitric oxide, and 3 for hydrocarbons. The relationships between hot spots and explanatory variables were investigated using graphical and statistical methods. Average speed, average acceleration, standard deviation of speed, percent of time spent in cruise mode, minimum speed, maximum acceleration, and maximum power have statistically significant associations with vehicle emissions and influence emissions hot spots. For example, stop-and-go traffic conditions that result in sudden changes in speed, and traffic patterns with high accelerations, are shown to generate hot spots. The implications of this work for future model development and applications to environmental management are discussed.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2--A case study in Beijing

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 x 10(7) L, 4.28 x 10(4) t, 1.97 x 10(3) t, 0.28 x 10(3) t, and 0.14 x 10(3) t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Modeling the impacts of traffic emissions on air toxics concentrations near roadways

The dispersion formulation incorporated in the U.S. Environmental Protection Agency's AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwind receptors ranging from 10-m to 100-m from the edge of a major highway in Raleigh, North Carolina. The contributions are computed using the following steps: 1) Evaluate dispersion model estimates with 10-min averaged NO data measured at 7 m and 17 m from the edge of the road during a field study conducted in August, 2006; this step determines the uncertainty in model estimates. 2) Use dispersion model estimates and their uncertainties, determined in step 1, to construct pseudo-observations. 3) Fit pseudo-observations to actual observations of VOC concentrations measured during five periods of the field study. This provides estimates of the contributions of traffic emissions to the VOC concentrations at the receptors located from 10 m to 100 m from the road. In addition, it provides estimates of emission factors and background concentrations of the VOCs, which are supported by independent estimates from motor vehicle emissions models and regional air quality measurements. The results presented in the paper demonstrate the suitability of the formulation in AERMOD for estimating concentrations associated with mobile source emissions near roadways. This paper also presents an evaluation of the key emissions and dispersion modeling inputs necessary for conducting assessments of local-scale impacts from traffic emissions.     

A vehicle emissions system using a car simulator and a geographical information system: Part 1--System description and testing

A methodology for estimating vehicular emissions comprising a car simulator, a basic traffic model, and a geographical information system is capable of estimating vehicle emissions with high time and space resolution. Because of the extent of the work conducted, this article comprises two sections: In Part 1 of this work, we describe the system and its components and use examples for testing it. In Part 2 we will study in more detail the emissions of the sample fleet using the system and will make comparisons with another emission model. The experimental data for the car simulator is obtained using on-board measuring equipment and laboratory Fourier transform IR (FTIR) measurements with a dynamometer following typical driving cycles. The car simulator uses this data to generate emission factors every second. These emission factors, together with information on car activity and velocity profiles of highways and residential and arterial roads in Mexico City in conjunction with a basic traffic model, provide emissions per second of a sample fleet. A geographical information system is used to localize these road emissions.     

Road traffic impact on urban water quality: a step towards integrated traffic,air and stormwater modelling

Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area.     

Concentrations of ammonia and nitrogen dioxide at roadside verges, and their contribution to nitrogen deposition

Bimonthly integrated measurements of NO2 and NH3 have been made over one year at distances up to 10 m away from the edges of roads across Scotland, using a stratified sampling scheme in terms of road traffic density and background N deposition. The rate of decrease in gas concentrations away from the edge of the roads was rapid, with concentrations falling by 90% within the first 10 m for NH3 and the first 15 m for NO2. The longer transport distance for NO2 reflects the production of secondary NO2 from reaction of emitted NO and O3. Concentrations above the background, estimated at the edge of the traffic lane, were linearly proportional to traffic density for NH3 (microg NH3 m(-3) = 1 x 10(-4) x numbers of cars per day), reflecting emissions from three-way catalysts. For NO2, where emissions depend strongly on vehicle type and fuel, traffic density was calculated in terms of 'car equivalents'; NO2 concentrations at the edge of the traffic lane were proportional to the number of car equivalents (microg NO2 m(-3) = 1 x 10(-4) x numbers of car equivalents per day). Although absolute concentrations (microg m(-3)) of NH3 were five times smaller than for NO2, the greater deposition velocity for NH3 to vegetation means that approximately equivalent amounts of dry N deposition to road side vegetation from vehicle emissions comes from NH3 and NO2. Depending on traffic density, the additional N deposition attributable to vehicle exhaust gases is between 1 and 15 kg N ha(-1) y(-1) at the edge of the vehicle lane, falling to 0.2-10 kg N ha(-1) y(-1) at 10 m from the edge of the road.     

Source apportionment of suspended particulate matter at two traffic junctions in Mumbai,India

Very high concentration of suspended particulate matter (SPM) is observed at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used for quantitative apportionment of the sources contributing to the SPM at two traffic junctions (Sakinaka and Gandhinagar) in Mumbai, India. Varimax rotated factor analysis identified (qualitative) five possible sources; road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. A quantitative estimation by FA-MR model indicated that road dust contributed to 41%, vehicular emissions to 15%, marine aerosols to 15%, metal industries to 6% and coal combustion to 6% of the SPM observed at Sakinaka traffic junction. The corresponding figures for Gandhinagar traffic junction are 33%, 18%, 15%, 8% and 11%, respectively. Due to limitation in source marker elements analysed about 16% of the remaining SPM at these two traffic junctions could not be apportioned to any possible sources by this technique. Of the observed lead in the SPM, FA-MR apportioned 62% to vehicular emissions, 17% to road dust, 11% to metal industries, 7% to coal combustion and 3% to marine aerosols at Gandhinagar traffic junction and about a similar apportionment for lead in SPM at Sakinaka traffic junction.     

Longitudinal emissions evaluation of mixed (cooperative) adaptive cruise control traffic flow and its relationship with stability

ABSTRACT

Cooperative adaptive cruise control (CACC) vehicles need vehicle-to-vehicle (V2 V) communication to achieve CACC function. When a CACC vehicle follows a manual-driven vehicle (MDV) without V2 V communication, it needs degenerate to adaptive cruise control (ACC). By using real experiments, California PATH program indicated that ACC vehicles are apt to be unstable, which may have negative influence on fuel consumption and traffic emissions. Hence, this paper studies the impacts of the mixed CACC-MDV traffic on fuel consumption and emissions, by taking into consideration partial degenerations from stable CACC vehicles to unstable ACC vehicles. To deal with this, microscopic simulations were adopted by using car-following models. Then, an appropriate emission model was used for evaluating the emission impacts under different CACC market penetration rates (MPRs). In order to obtain reliable evaluation results, the models validated by PATH program using real experimental data were employed as the CACC and ACC car-following models. In addition, we also analytically investigated stability of the mixed traffic flow under different CACC MPRs, in order to explore its relationship with the emission impacts. The results show that the fuel consumption and emissions firstly increase and then decrease with the increase of the CACC MPR. This means the mixed traffic under some ranges of CACC MPRs will produce more fuel consumption and emissions, compared with the full MDVs traffic. It indicates that stability situations of the mixed traffic qualitatively influence the impact trend of CACC MPRs on fuel consumption and emissions. Then, V2 V communication equipments on MDVs are not only encouraging but also essential to avoid the deterioration of fuel consumption and emissions of the mixed traffic flow.     

A Vehicle Emissions System Using a Car Simulator and a Geographical Information System: Part 1—System Description and Testing

Abstract

A methodology for estimating vehicular emissions comprising a car simulator, a basic traffic model, and a geographical information system is capable of estimating vehicle emissions with high time and space resolution. Because of the extent of the work conducted, this article comprises two sections: In Part 1 of this work, we describe the system and its components and use examples for testing it. In Part 2 we will study in more detail the emissions of the sample fleet using the system and will make comparisons with another emission model. The experimental data for the car simulator is obtained using on-board measuring equipment and laboratory Fourier transform IR (FTIR) measurements with a dynamometer following typical driving cycles. The car simulator uses this data to generate emission factors every second. These emission factors, together with information on car activity and velocity profiles of highways and residential and arterial roads in Mexico City in conjunction with a basic traffic model, provide emissions per second of a sample fleet. A geographical information system is used to localize these road emissions.     

Effects of advanced traffic signal status warning systems on vehicle emission reductions at signalized intersections

Signalized intersections have been identified as vehicle emission hotspots, where drivers decelerate, idle, and accelerate their vehicles in response to signal changes. Advanced traffic signal status warning systems (ATSSWSs) can be applied to reduce traffic emissions at intersections by mitigating unnecessary braking and acceleration. In this study, two types of ATSSWSs, variable message sign (VMS) based and vehicle-to-infrastructure (V2I) based, were designed, and their environmental effectiveness was evaluated through driving simulator-based experiments. Three scenarios were designed and tested: (1) baseline without an ATSSWS, (2) with the VMS-based ATSSWS, and (3) with the V2I-based ATSSWS. The Motor Vehicle Emission Simulator model was used to evaluate and compare the environmental effectiveness of these two types of ATSSWSs. The results indicate that the proposed ATSSWSs can reduce traffic emissions at signalized intersections. In particular, the V2I-based ATSSWS can substantially reduce CO₂, NOx, CO, and HC emissions. The results will help transportation practitioners with implementing advanced driver information systems and decision making on emission reduction policies.

Implications: Signalized intersection has been identified as one of hottest spots for vehicle emissions where signal control causes vehicles to frequently decelerate, idle, and accelerate. Advanced Traffic Signal Status Warning Systems (ATSSWS) can be applied to reduce traffic emission at intersections by decreasing vehicles’ unnecessary brakes and accelerations. The results of this study will assist transportation practitioners in implementing advanced driver information systems and making decisions on emission reduction policies.     

Development of operating mode distributions for different types of roadways under different congestion levels for vehicle emission assessment using MOVES

The Motor Vehicle Emission Simulator (MOVES) quantifies emissions as a function of vehicle modal activities. Hence, the vehicle operating mode distribution is the most vital input for running MOVES at the project level. The preparation of operating mode distributions requires significant efforts with respect to data collection and processing. This study is to develop operating mode distributions for both freeway and arterial facilities under different traffic conditions. For this purpose, in this study, we (1) collected/processed geographic information system (GIS) data, (2) developed a model of CO₂ emissions and congestion from observations, (3) implemented the model to evaluate potential emission changes from a hypothetical roadway accident scenario. This study presents a framework by which practitioners can assess emission levels in the development of different strategies for traffic management and congestion mitigation.

Implications: This paper prepared the primary input, that is, the operating mode ID distribution, required for running MOVES and developed models for estimating emissions for different types of roadways under different congestion levels. The results of this study will provide transportation planners or environmental analysts with the methods for qualitatively assessing the air quality impacts of different transportation operation and demand management strategies.