Characterizing driving patterns for Zhuhai for traffic emissions estimation

Zhuhai, a relatively less developed city on the western coast of the Pearl River Delta (PRD) of China, is planning to undergo major development in coming years. A Hong Kong-Zhuhai-Macao Bridge project has been approved by the Central Government of China. The project will have great impact on the driving pattern and vehicular emissions to the city. This baseline study collected speed-time data of two instrumented private cars in morning and evening periods, as well as a daytime nonpeak period of >10 consecutive days in the spring and winter of 2003. The authors used the microwave speed sensor and global positioning system installed in the instrumented cars and used car-chasing technique to perform the data collection. They used the statistical package SPSS to assess the consistency, as well as to evaluate the variability of the data. Nine parameters, namely, average speed, average running speed, average acceleration rate, average deceleration rate, mean length of a driving period, time proportions of driving modes, average number of acceleration-deceleration changes, root mean square acceleration, and positive acceleration kinetic energy are calculated to represent the driving characteristics. A driving cycle for private cars was developed. If emission tests were conducted using the Zhuhai driving cycle, the level of vehicle emissions measured is likely to be in between that of the Federal Test Procedure (FTP) cycle and the Melbourne Peak cycle.     

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.     

Emission Factors for Gasoline Light-Duty Vehicles: Experimental Program in Santiago,Chile

ABSTRACT

The emission inventory of the city of Santiago, Chile, related to mobile sources was built up using constant emission factors extracted from international literature. To improve the estimate of mobile source emissions, an experimental program was designed, consisting of transient tests on a chassis dynamometer over a sample of about 166 vehicles, applying 9 local driving cycles with average speeds of 3-80 km/hr, and experimentally determined in previous research carried out by the authors. An analysis of the influence of fuel inlet technology, and a year time-length model over emissions, was undertaken. We proposed emission factors as a function of average speed and of CO, THC, and NO_x for catalytic and noncatalytic light-duty gasoline vehicles, disaggregated on commercial and private cars. A comparative analysis with emission factors obtained for the application of the COPERT II and AP-42 models was also presented. Our current analysis gives solid evidence indicating that to obtain a reasonable accuracy on emission estimates and calculations, local emission factors must be used.     

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.     

Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles

Within the European research project ARTEMIS, significant works have been conducted to analyse the hot emissions of pollutant from the passenger cars regarding the driving cycles and to propose modelling approaches taking into account large but heterogeneous datasets recorded in Europe. The review and analysis of a large range of test cycles enabled first the building-up of a set of contrasted cycles specifically designed for characterizing the influence of the driving conditions. These cycles were used for the measurement of the pollutants emission rates from nine passenger cars on a chassis dynamometer.Emissions measured on 30 vehicles tested on cycles adapted to their motorization (i.e., cycles for high- or low-powered cars, inducing thus a significant difference in the dynamic) were also considered for analysing the influence of the cycles and of the kinematic parameters on the hot emission rates of the regulated pollutants (CO, HC, NO_x, CO₂, PM). An analyses of variance demonstrated the preponderance of the driving type (urban, rural road, motorway), of the vehicle category (fuel, emission standard) and emitting status (high/normal emitter) and thus the pertinence of analysing and modelling separately the corresponding emissions. It also demonstrated that Urban driving led systematically to high diesel emission rates and to high CO₂, HC and NO_x emissions from petrol cars. Congested driving implied high CO₂ (diesel and petrol) and high diesel NO_x emission. On motorway, the very high speeds generated high CO₂, while unsteady speeds induced diesel NO_x and petrol CO over-emissions. A search for pertinent kinematic parameters showed that urban diesel emissions were mostly sensitive to stops and speed parameters, while petrol emissions were rather sensitive to acceleration parameters. On the motorway, diesel NO_x and CO₂ emissions rates increased with the speed variability and occurrence of high speeds, while CO₂ and CO over-emission from petrol cars were linked to the occurrence of strong acceleration at high speeds.A modelling approach based on partial least square regression was tested, which demonstrates its ability to discriminate satisfactorily the emissions according to dynamic related parameters and in particular when considering the two-dimensionnal distribution of instantaneous speed and acceleration.Finally, a strategy was proposed to analyse the large but heterogeneous set of hot emission data collected within the ARTEMIS project. The approach consisted in analysing the similarity of the numerous cycles as regards their kinematic, grouping them into reference test patterns through an automatic clustering, and then computing reference emissions for these patterns. These principles enabled the development of a method to compute the emissions at a low spatial scale, i.e. the so-called traffic situation approach, which was implemented in the European Artemis model for estimating the cars’ pollutant emissions.     

An exploratory analysis comparing a stochastic driving cycle to California's regulatory cycle

As the fundamental building block of the emissions estimation process, a driving cycle needs to be representative of real-world driving behavior. The driving cycle construction method becomes crucial for generating a representative driving cycle. In this paper, we revisit the Unified Cycle's (i.e., the LA92 driving cycle) construction method. The California Air Resources Board's Unified Cycle used a “microtrips” approach, a speed–acceleration frequency distribution plot, and a quasi-random selection mechanism to build the driving cycle. There is concern that the Unified Cycle does not reflect the true driving patterns due to the identified flaws in the construction methodology. Treating a driving trace as a stochastic process, we construct a new driving cycle (LA01) with the same driving data originally used to build the Unified Cycle. We then compare the two driving cycles with the sample data set with respect to the durations and intensities of the modal events. The new driving cycle is found to better replicate the modal events observed in the sample data. A comparison of average road power values between the sample data, LA01, and the Unified Cycle also confirms the effect of fine-scale driving on emissions. These differences result from the different construction approaches and can be expected to affect emissions inventory estimation.     

Emission factors for gasoline light-duty vehicles: experimental program in Santiago, Chile

The emission inventory of the city of Santiago, Chile, related to mobile sources was built up using constant emission factors extracted from international literature. To improve the estimate of mobile source emissions, an experimental program was designed, consisting of transient tests on a chassis dynamometer over a sample of about 166 vehicles, applying 9 local driving cycles with average speeds of 3-80 km/hr, and experimentally determined in previous research carried out by the authors. An analysis of the influence of fuel inlet technology, and a year time-length model over emissions, was undertaken. We proposed emission factors as a function of average speed and of CO, THC, and NOx for catalytic and noncatalytic light-duty gasoline vehicles, disaggregated on commercial and private cars. A comparative analysis with emission factors obtained for the application of the COPERT II and AP-42 models was also presented. Our current analysis gives solid evidence indicating that to obtain a reasonable accuracy on emission estimates and calculations, local emission factors must be used.     

Characterization of In-Use Light-Duty Gasoline Vehicle Emissions by Remote Sensing in Beijing: Impact of Recent Control Measures

Abstract

China’s national government and Beijing city authorities have adopted additional control measures to reduce the negative impact of vehicle emissions on Beijing’s air quality. An evaluation of the effectiveness of these measures may provide guidance for future vehicle emission control strategy development. In-use emissions from light-duty gasoline vehicles (LDGVs) were investigated at five sites in Beijing with remote sensing instrumentation. Distance-based mass emission factors were derived with fuel consumption modeled on real world data. The results show that the recently implemented aggressive control strategies are significantly reducing the emissions of on-road vehicles. Older vehicles are contributing substantially to the total fleet emissions. An earlier program to retrofit pre-Euro cars with three-way catalysts produced little emission reduction. The impact of model year and driving conditions on the average mass emission factors indicates that the durability of vehicles emission controls may be inadequate in Beijing.     

Development of the Tehran car driving cycle

This paper describes the development of the car driving cycle for the capital city of Tehran. Driving cycle is an essential requirement for the evaluation of the exhaust emissions using the chassis dynamometer test. In this study, the driving data are collected from several cars under real traffic conditions. The method used in this study for data analysis is based on the definition and the classification of the microtrips. The developed cycle is named TEH_CAR. The results reveal that the TEH_CAR cycle characteristics are close to the transient FTP cycle and different from the synthetic ECE cycle.     

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.     

Analysis of Remote Sensing Errors of Omission and Commission Under FTP Conditions

Abstract

Second-by-second modal emissions data from a 73-vehicle fleet of 1990 and 1991 light duty cars and trucks driven on the Federal Test Procedure (FTP) driving cycle were examined to determine remote sensing errors of commission in identifying high emissions vehicles. Results are combined with a similar analysis of errors of omission based on modal FTP data from high emissions vehicles. Extremely low errors of commission combined with modest errors of omission indicate that remote sensing should be very effective in isolating high CO and HC emitting vehicles in a fleet of late model vehicles on the road.     

Development of the typical driving cycle for buses in Hanoi,Vietnam

This paper develops a typical driving cycle for buses in Hanoi that does not require the deconstruction of the natural driving patterns. Real velocity–time data were collected along 15 routes in the inner city. The raw velocity–time series were preprocessed to remove errors, and smooth and denoise the data. These data, then, were tested for stationary behavior before being used in the construction of the driving cycle based on Markov chain theory. The 14 representative parameters of the driving cycle, including vehicle-specific power, which were extracted from 33 driving cycle parameters using the hierarchical agglomerative clustering method, were used to integrate the features of realistic driving patterns into the typical driving cycle. The conformity of the developed driving cycle with the real-world driving data was evaluated by the speed–acceleration frequency distribution (SAFD). A typical driving cycle for buses in Hanoi with a SAFD of 13.2% was developed. This is the first driving cycle developed for buses in Vietnam.

Implications: A typical driving cycle was developed for the first time for buses in Hanoi. With the deviation in speed-acceleration frequency distribution (SAFD) reaching to 13.2%, the developed driving cycle reflects well the overall real-world driving data in the city. This driving cycle, therefore, can be applied for the development of the country-specific emission factors and emission inventories for buses which are a very good tool as well as useful information for integrated air quality management in Hanoi.     

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.     

A Modal Approach to Vehicular Emissions and Fuel Consumption Model Development

Abstract

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On‐road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on‐road measurements are feasible for the development of modal emission and fuel consumption models.     

Development of a driving cycle for Hong Kong

The paper presents the results of the development of a standard driving cycle in the urban areas of Hong Kong. On-road speed–time data were collected by an instrumented diesel vehicle along two fixed routes located in two urban districts in Hong Kong. The collected data were analyzed and compared with mandatory driving cycles used elsewhere. It was found that none of these mandatory cycles could satisfactorily describe the driving characteristics in Hong Kong. A unique driving cycle was therefore developed for Hong Kong. The cycle was built up by extracting parts of the on-road speed data such that the summary statistics of the sample are close to that derived from the data population of the test runs.     

An Evaluation of the Aerometric Probe

Exhaust emissions from automobiles in a low-altitude city will be compared with emissions from autos in a high-altitude city (Denver, Colorado). The comparison will be based on samples collected from thirty five cars driven under actual road conditions in each city.

Results will be discussed on the basis of CO, CO₂ and hydrocarbon concentrations versus average route speeds and on pounds of CO, CO₂ and hydrocarbons, emitted per mile, versus average route speed.     

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.     

Modeling transit bus fuel consumption on the basis of cycle properties

A method exists to predict heavy-duty vehicle fuel economy and emissions over an "unseen" cycle or during unseen on-road activity on the basis of fuel consumption and emissions data from measured chassis dynamometer test cycles and properties (statistical parameters) of those cycles. No regression is required for the method, which relies solely on the linear association of vehicle performance with cycle properties. This method has been advanced and examined using previously published heavy-duty truck data gathered using the West Virginia University heavy-duty chassis dynamometer with the trucks exercised over limited test cycles. In this study, data were available from a Washington Metropolitan Area Transit Authority emission testing program conducted in 2006. Chassis dynamometer data from two conventional diesel buses, two compressed natural gas buses, and one hybrid diesel bus were evaluated using an expanded driving cycle set of 16 or 17 different driving cycles. Cycle properties and vehicle fuel consumption measurements from three baseline cycles were selected to generate a linear model and then to predict unseen fuel consumption over the remaining 13 or 14 cycles. Average velocity, average positive acceleration, and number of stops per distance were found to be the desired cycle properties for use in the model. The methodology allowed for the prediction of fuel consumption with an average error of 8.5% from vehicles operating on a diverse set of chassis dynamometer cycles on the basis of relatively few experimental measurements. It was found that the data used for prediction should be acquired from a set that must include an idle cycle along with a relatively slow transient cycle and a relatively high speed cycle. The method was also applied to oxides of nitrogen prediction and was found to have less predictive capability than for fuel consumption with an average error of 20.4%.     

Real-world and type-approval emission evolution of passenger cars

The investigation of several passenger car generations with gasoline engines shows that the emissions depend very strongly on the driving cycle. Official type approval cycles allow just very inaccurate predications about their real-world emissions. The measured gasoline vehicles have up to factor 11 higher real-life emissions than in type approval cycles. However, a clear reduction of real-world emissions can be seen over the different investigated generations of gasoline cars. In addition, it can be seen that the cold start emissions depend strongly on ambient temperature levels for all generations of cars and that the cold start accounts for an increasing part of the total pollutant emissions. As an extreme example, the cold start hydrocarbon emissions of Euro-3 cars at –20°C ambient temperature correspond approximately to those of 1,000 km driving with warm engines.     

Modelling fuel consumption and pollutant emissions based on real-world driving patterns: the HBEFA approach

Emissions of passenger cars and light-duty vehicles with complex exhaust gas after-treatment are difficult to predict, especially if the prediction is only based on kinematic parameters without vehicle-specific data. A new method for modelling fleet emission factors based on testbench data is presented. It has been used for modern passenger cars and light-duty vehicles (EURO-2 and -3) in the new version 2.1 of the German-Austrian-Swiss Handbook Emission Factors for Road Transport (HBEFA). The new method, not relying on vehicle-specific data, avoids decomposing the measured real-world driving behaviour and all associated uncertainties. Emission factors can be predicted for any given driving pattern which is characterised through kinematic parameters or representative time series of vehicle speed. The methodology determines the linear combination of measured driving patterns that is most representative for the driving pattern whose emissions are to be predicted. The approach is illustrated using testbench real-world measurements of 44 passenger cars of technology stages EURO-2 and -3.