Comprehensive analysis of the carbon impacts of vehicle intelligent speed control

In recent years sophisticated technologies have been developed to control vehicle speed based on the type of road the vehicle is driven on using Global Positioning Systems and in-car technology that can alter the speed of the vehicle. While reducing the speed of road vehicles is primarily of interest from a safety perspective, vehicle speed is also an important determinant of vehicle emissions and thus these technologies can be expected to have impacts on a range of exhaust emissions. This work analyses the results from a very large, comprehensive field trial that used 20 instrumented vehicles with and without speed control driven almost 500,000 km measuring vehicle speed at 10 Hz. We develop individual vehicle modal emissions models for CO₂ for 30 Euro III and Euro IV cars at a 1-Hz time resolution. Generalized Additive Models were used to describe how emissions from individual vehicles vary depending on their driving conditions, taking account of variable interactions and time-lag effects. We quantify the impact that vehicle speed control has on-vehicle emissions of CO₂ by road type, fuel type and driver behaviour. Savings in CO₂ of ≈6% were found on average for motorway-type roads when mandatory speed control was used compared with base case conditions. For most other types of road, speed control has very little effect on emissions of CO₂ and in some cases can result in increased emissions for low-speed limit urban roads. We also find that there is on average a 20% difference in CO₂ emission between the lowest and highest emitting driver, which highlights the importance of driver behaviour in general as a means of reducing emissions of CO₂.     

Turbulent Diffusion Behind Vehicles: Effect of Traffic Speed on Pollutant Concentrations

Recent theoretical and experimental investigations Indicate that turbulent diffusion behind moving vehicles Is Influenced by the speed of the vehicle. Vertical wake induced turbulent diffusion, explicitly treated in the numerical ROADWAY model, is proportional to the square of the wind speed relative to the moving vehicle. Hence, the model predictions of turbulent mixing and pollutant concentrations on and downwind of a roadway are dependent upon the traffic speed. It Is expected from theoretical considerations that the effect of vehicle speed on pollutant concentrations will be more significant during stable atmospheric conditions, because in neutral and unstable conditions the vehicle-wake turbulence is quickly masked by the ambient turbulence. In this study, experimental data are utilized to evaluate the theoretical predictions of the effects of traffic speed on the ambient pollutant concentrations. The effects of vehicle speed upon ambient concentrations are investigated through wind tunnel experiments and field studies that used dual tracers. Consistent with predictions of the ROADWAY model, data obtained near the Long Island Expressway indicate that the influence of traffic speed on the ambient pollutant concentrations Is not significant during unstable and neutral conditions. The Long Island experiment did not provide sufficient field data to assess the model predictions of the traffic speed effect during stable atmospheric conditions.     

Evaluating speed differences between passenger vehicles and heavy trucks for transportation-related emissions modeling

Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for > 7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on-road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on-road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.     

Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for Transportation-Related Emissions Modeling

Abstract

Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for >7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on‐road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on‐road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.     

Scattering cross-section emission factors for visibility and radiative transfer applications: military vehicles traveling on unpaved roads

Emission factors for particulate matter (PM) are generally reported as mass emission factors (PM mass emitted per time or activity) as appropriate for air quality standards based on mass concentration. However, for visibility and radiative transfer applications, scattering, absorption, and extinction coefficients are the parameters of interest, with visibility standards based on extinction coefficients. These coefficients (dimension of inverse distance) equal cross-section concentrations, and, therefore, cross-section emission factors are appropriate. Scattering cross-section emission factors were determined for dust entrainment by nine vehicles, ranging from light passenger vehicles to heavy military vehicles, traveling on an unpaved road. Each vehicle made multiple passes at multiple speeds while scattering and absorption coefficients, wind velocity and dust plume profiles, and additional parameters were measured downwind of the road. Light absorption of the entrained PM was negligible, and the light extinction was primarily caused by scattering. The resulting scattering cross-section emission factors per vehicle kilometer traveled (vkt) range from 12.5 m2/vkt for a slow (16 km/ hr), light (1176 kg) vehicle to 3724 m2/vkt for a fast (64 km/hr), heavy (17,727 kg) vehicle and generally increase with vehicle speed and mass. The increase is approximately linear with speed, yielding emission factors per vkt and speed ranging from 4.2 m2/(vkt km/hr) to 53 m2/(vkt km/hr). These emission factors depend approximately linearly on vehicle mass within the groups of light (vehicle mass < or =3100 kg) and heavy (vehicle mass >8000 kg) vehicles yielding emission factors per vkt, speed, and mass of 0.0056 m2/(vkt km/hr kg) and 0.0024 m2/(vkt km/hr kg), respectively. Comparison of the scattering cross-section and PM mass emission factors yields average mass scattering efficiencies of 1.5 m2/g for the light vehicles and of 0.8 m2/g for the heavy vehicles indicating that the heavy vehicles entrain larger particles than the light vehicles.     

On-road motor vehicle emissions and fuel consumption in urban driving conditions

This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NOx, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.     

On-Road Motor Vehicle Emissions and Fuel Consumption in Urban Driving Conditions

ABSTRACT

This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NO_x, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/ sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.     

On-road emission characteristics of heavy-duty diesel vehicles in Shanghai

On-road vehicle tests of nine heavy-duty diesel trucks were conducted using SEMTECH-D, an emissions measuring instrument provided by Sensors, Inc. The total length of roads for the tests was 186 km. Data were obtained for 37,255 effective driving cycles, including 17,216 on arterial roads, 15,444 on residential roads, and 4595 on highways. The impacts of speed and acceleration on fuel consumption and emissions were analyzed. Results show that trucks spend an average of 16.5% of the time in idling mode, 25.5% in acceleration mode, 27.9% in deceleration mode, and only 30.0% at cruise speed. The average emission factors of CO, total hydrocarbons (THC), and NO_x for the selected vehicles are (4.96±2.90), (1.88±1.03) and (6.54±1.90) g km⁻¹, respectively. The vehicle emission rates vary significantly with factors like speed and acceleration. The test results reflect the actual traffic situation and the current emission status of diesel trucks in Shanghai. The measurements show that low-speed conditions with frequent acceleration and deceleration, particularly in congestion conditions, are the main factors that aggravate vehicle emissions and cause high emissions of CO and THC. Alleviating congestion would significantly improve vehicle fuel economy and reduce CO and THC emissions.     

Changing speed-VMT distributions: the effects on emissions inventories and conformity

The emissions factor modeling component of the motor vehicle emissions inventory (MVEI) modeling suite is currently being revised by the California Air Resources Board (CARB). One of the proposed changes in modeling philosophy is a shift from using link-based travel activity data to trip-based travel data for preparing mobile emissions inventories. Also as part of the revisions, new speed correction factors (SCFs) will be developed by CARB for the revised model. The new SCFs will be derived from vehicle emissions on 15 new driving cycles, each constructed to represent a typical trip at a specific average speed. This paper discusses how the new SCFs will affect transportation conformity and emissions inventory development, and evaluates the differences in total emissions produced by trip-based and link-based distributions of speed and vehicle miles of travel (VMT). We simulated both link-based and trip-based speed-VMT distributions using travel data from the Sacramento and San Diego travel demand models. On the basis of the simulation results, there is reason to expect that mobile emissions inventories constructed using the proposed trip-based philosophy will differ markedly from those constructed in the current manner. Noting that results may vary by region, increases are expected in the CO and HC inventory levels, with concomitant decreases in the NOx mobile emissions inventories.     

街道峡谷机动车尾气污染扩散模拟与影响因子分析研究

为预测和分析街道峡谷污染物浓度,研究了街道峡谷污染物浓度影响因子.利用重庆市交通干线街道峡谷两侧NOx浓度的监测数据,验证了街道峡谷机动车尾气污染扩散模型--OSPM模型.风速转换系数修正后的OSPM模型的模拟值与实测值的R达0.862 58;风场因子验证了风速转换系数修正后的OSPM模型能较好地模拟重庆市街道峡谷的污染物浓度,一定程度上能满足环境空气质量评价要求.同时,通过分析OSPM模型的影响因子,提出了控制街道峡谷机动车尾气污染状况的建议.     

Sensitivity Analysis and Evaluation of MicroFacCO: A Microscale Motor Vehicle Emission Factor Model for CO Emissions

ABSTRACT

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting realtime site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by ~67% at ambient temperatures of 45 °F and ≥95 °F in comparison with an ambient temperature of 75 °F.     

Changing Speed-VMT Distributions: The Effects on Emissions Inventories and Conformity

ABSTRACT

The emissions factor modeling component of the motor vehicle emissions inventory (MVEI) modeling suite is currently being revised by the California Air Resources Board (CARB). One of the proposed changes in modeling philosophy is a shift from using link-based travel activity data to trip-based travel data for preparing mobile emissions inventories. Also as part of the revisions, new speed correction factors (SCFs) will be developed by CARB for the revised model. The new SCFs will be derived from vehicle emissions on 15 new driving cycles, each constructed to represent a typical trip at a specific average speed. This paper discusses how the new SCFs will affect transportation conformity and emissions inventory development, and evaluates the differences in total emissions produced by trip-based and link-based distributions of speed and vehicle miles of travel (VMT).

We simulated both link-based and trip-based speed-VMT distributions using travel data from the Sacramento and San Diego travel demand models. On the basis of the simulation results, there is reason to expect that mobile emissions inventories constructed using the proposed trip-based philosophy will differ markedly from those constructed in the current manner. Noting that results may vary by region, increases are expected in the CO and HC inventory levels, with concomitant decreases in the NO_x mobile emissions inventories.     

Sensitivity analysis and evaluation of MicroFacPM: a microscale motor vehicle emission factor model for particulate matter emissions

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper titled "Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle Emissions". The emission rates discussed are in mass per unit distance with the model providing estimates of fine particulate matter (PM2.5) and coarse particulate matter. This paper complements the companion paper by presenting a sensitivity analysis of the model to input variables and evaluation model outputs using data from limited field studies. The sensitivity analysis has shown that MicroFacPM emission estimates are very sensitive to vehicle fleet composition, speed, and the percentage of high-emitting vehicles. The vehicle fleet composition can affect fleet emission rates from 8 mg/mi to 1215 mg/mi; an increase of 5% in the smoking (high-emitting) current average U.S. light-duty vehicle fleet (compared with 0%) increased PM2.5 emission rates by -272% for 2000; and for the current U.S. fleet, PM2.5 emission rates are reduced by a factor of -0.64 for speeds >50 miles per hour (mph) relative to a speed of 10 mph. MicroFacPM can also be applied to examine the contribution of emission rates per vehicle class, model year, and sources of PM. The model evaluation is presented for the Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA, and some limited evaluations at two locations: Sepulveda Tunnel, Los Angeles, CA, and Van Nuys Tunnel, Van Nuys, CA. In general, the performance of MicroFacPM has shown very encouraging results.     

Simulation of the evolution of particle size distributions in a vehicle exhaust plume with unconfined dilution by ambient air

Over the past several years, numerous studies have linked ambient concentrations of particulate matter (PM) to adverse health effects, and more recent studies have identified PM size and surface area as important factors in determining the health effects of PM. This study contributes to a better understanding of the evolution of particle size distributions in exhaust plumes with unconfined dilution by ambient air. It combines computational fluid dynamics (CFD) with an aerosol dynamics model to examine the effects of different streamlines in an exhaust plume, ambient particle size distributions, and vehicle and wind speed on the particle size distribution in an exhaust plume. CFD was used to calculate the flow field and gas mixing for unconfined dilution of a vehicle exhaust plume, and the calculated dilution ratios were then used as input to the aerosol dynamics simulation. The results of the study show that vehicle speed affected the particle size distribution of an exhaust plume because increasing vehicle speed caused more rapid dilution and inhibited coagulation. Ambient particle size distributions had an effect on the smaller sized particles (approximately 10 nm range under some conditions) and larger sized particles (>2 microm) of the particle size distribution. The ambient air particle size distribution affects the larger sizes of the exhaust plume because vehicle exhaust typically contains few particles larger than 2 microm. Finally, the location of a streamline in the exhaust plume had little effect on the particle size distribution; the particle size distribution along any streamline at a distance x differed by less than 5% from the particle size distributions along any other streamline at distance x.     

Turbulent diffusion behind vehicles: Experimentally determined influence of vortex pair in vehicle wake

The wake of a moving vehicle was simulated using a wind tunnel with a moving floor. The vehicle models, both block-shaped and ‘true’ scale models of actual automobiles, were held in a fixed position while the floor moved at the upstream air speed. This simulates an automobile travelling on a straight highway in still ambient air.Vertical and lateral profiles of mean and fluctuating velocities and mean tracer concentration were obtained. Profiles were taken at distances of 15–60 model heights downstream. Two exhaust source positions were used: at the center of the rear of the vehicle and on the side just behind the rear wheel. It was found that the models of true vehicles induce a pair of vortices in the wake that modify the velocity and concentration patterns in a minor way from that of the block-shaped vehicle.     

隧道实验测定南京市机动车PM_（10）排放因子

选取南京城市隧道进行机动车PM10平均排放因子的测试研究。采用质量平衡模型和多元线性回归方法计算了4种车型PM10的综合排放因子。结果表明：隧道内机动车PM10平均排放因子为0.347±0.100 g/（km·辆）;大型车的PM10排放因子远高于其他车型的排放因子,其次是中型车和摩托车,小型车最小,其综合排放因子分别为1.440 g/（km·辆）、0.850 g/（km·辆）、0.790 g/（km·辆）和0.320 g/（km.辆）;在车速相似的情况下,本隧道实验所测机动车的PM10排放因子与国内隧道实验结果相仿,却远大于国外隧道实验结果。     

机动车尾气VOCs排放特征及减排措施研究

分析了机动车尾气挥发性有机物(VOCs)的排放特征,发现尾气VOCs排放具有明显的日变化和季节变化特征。不同区域不同车型机动车尾气VOCs成分谱略有差异,轻型汽油车尾气VOCs中芳香烃和烷烃含量较高,柴油车烷烃含量较高。尾气排放受机动车保有量、行驶里程、维护保养水平、行驶速度和燃油标准、排放标准等因素影响。从优先控制汽油车、加快机动车更新、采取本地化减排措施、加强多元管理措施、提高科研水平等方面提出了针对性的减排措施。     

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.     

Vehicle Occupant Exposure to Carbon Monoxide

Abstract

This paper focuses on the auto commuting micro-environment and presents typical carbon monoxide (CO) concentrations to which auto commuters in central Riyadh, Saudi Arabia were exposed. Two test vehicles traveling over four main arterial roadways were monitored for inside and outside CO levels during eighty peak and off-peak hours extending over an eight month period. The relative importance of several variables which explained the variability in CO concentrations inside autos was also assessed. It was found that during peak hours auto commuters were exposed to mean CO levels that ranged from 30 to 40 ppm over trips that typically took between 25 to 40 minutes. The mean ratio of inside to outside CO levels was 0.84. Results of variance component analyses indicated that the most important variables affecting CO concentrations inside autos were, in addition to the smoking of vehicle occupants, traffic volume, vehicle speed, period of day and wind velocity. An increase in traffic volume from 1,000 to 5,000 vehicles per hour (vph) increased mean CO level exposure by 71 percent. An increase in vehicle speed from 14 to 55 km/h reduced mean CO exposure by 36 percent. The number of traffic interruptions had a moderate effect on mean concentrations of CO inside vehicles.     

At the Crossroads

ABSTRACT

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of “microtrips”, which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of “microtrips” isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate “microtrips” is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

IMPLICATIONS It is expected that the paper will provide a useful tool for modeling and analysis of vehicle fuel economy and emissions and for the design, optimization, and analysis of driving cycles for testing and vehicle fleet management.