BIVARIATE PROBIT MODEL OF ON-ROAD EMISSION …

BIVARIATE PROBIT MODEL OF ON-ROAD EMISSION MEASUREMENT OF PASSENGER CARS IN JAKARTA CITY

SUDARMANTO Budi Nugroho Doctor Candidate Transportation Engineering Laboratory, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi Hiroshima, 739-8529, Japan Phone and Fax: 81-82-424-6922; E-mail: [email protected]

Akimasa FUJIWARA Professor Transportation Engineering Laboratory, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi Hiroshima, 739-8529, Japan Phone and Fax: 81-82-424-6921; E-mail: [email protected]

Junyi ZHANG Associate Professor Transportation Engineering Laboratory, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi Hiroshima, 739-8529, Japan Phone and Fax: 81-82-424-6919; E-mail: [email protected]

Metin SENBIL COE Researcher Transportation Engineering Laboratory, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi Hiroshima, 739-8529, Japan Phone and Fax: 81-82-424-6919; E-mail: [email protected]

Abstract: The Jakarta’s 2005 bylaw on air pollution control, stipulates that all private car owners must get their vehicles’ emission tested twice a year. The vehicle owners will be given a certificate, as a requirement to extend the vehicle’s registration. This paper describes an initial analysis of vehicle characteristics which influence to emission testing results. Analysis was done by using On-road emission measurements at nine major roads in Jakarta city and its surrounding cities. We apply bivariate probit model for the likelihood of CO and HC emission violations given a set of vehicle characteristics. The vehicle age, non-sedan, fuel type, carburetor and lambda all play a significant role in determining the probability of emission test failure. However, we didn’t find a significant effect of engine size on HC emission test failure. The results of study can be used as a preliminary review of the implementation of new regulation in Jakarta city Key Words: Bivariate Probit, Major Roads, Jabodetabek 1. INTRODUCTION Based on the report of APMA (Hag, et al, 2002), the total estimation of Carbon Monoxide (CO) emitted from all activities in Jakarta city is around 686,864 ton/year or 48.6 % of total emission of five pollutants (PM, SO2, NOx, HC and CO). A joint study by Japan International Cooperation Agency (JICA) and Environmental Impact Management Agency (1997) revealed that private motor vehicles and motorcycles contribute for 50 % and 20 % of CO emissions in Jakarta respectively. Moreover, private motor vehicles altogether are responsible for approximately 40 percent of HC emissions (SEI, UNEP and Kei, 2002). Furthermore, in the study by Nugroho et al (2005), based on the estimation results for the major roads of Jakarta city in 2002, passenger car contributes 50 % of HC and 68 % of CO from mobile sources emissions. And so, the contribution of mobile sources is dominant compare to other emission sources (industry, open source, etc) in Jakarta city especially for parameter CO. Looking at

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

1377

the mobile sources, comparing among several types of vehicle category, passenger car emit CO and HC dominantly. Facing severe air quality deteriorations, in 2005, local government of Jakarta issued a bylaw on air pollution controls from mobile sources, which took effect since February 2006. Bylaw stipulates that all private car owners must get their vehicles’ emission tested biennially (Simamora A.P, 2006), which is popularly known as the inspection and maintenance (I/M) program. As an incentive, the bylaw states that the emissions certificate is required for extending the vehicle’s registration (BPLHD, 2005). In order to pass the emissions test, the vehicle emissions should be lower than the in-use vehicle standards set by a decree. The vehicle emission test is performed by authorized automobile workshops and technicians. If a vehicle passes the emissions test, owner is given a certificate and a sticker to be attached on the upper left side of the windshield. In contrast, if a vehicle fails in the emission test, it should be repaired or should undergo maintenance procedures to bring emissions down to the allowable emissions levels. However, there have been ongoing debates on the effectiveness of I/M program to reduce the mobile emissions. Studies by Hubbard (1997), Wasburn et. al. (2001), and Bin (2003) have criticized I/M programs based on three points mainly. First, it has been argued that I/M programs are an inefficient use of resources to achieve air quality objectives. It is also inconvenient to the vast majority of the driving population. Second, the I/M program is not the most effective way to identify gross polluting vehicles. Especially, idle-mode test, the I/M test procedure for Jakarta city, does not account for the real world driving conditions such as acceleration and deceleration cycles. Thus vehicles passing the emissions test may still be gross polluters in the real world driving conditions. Third, the programs have mainly failed to provide drivers with incentives to minimize their vehicle emissions (Washburn et al., 2001). Additionally, many drivers tamper with engines and emissions control procedures in order to pass the emission test because of high costs of repair or maintenance costs of high emitting vehicles. In this study, we first attempt to analyze in-use vehicle emissions based on the data collected by idle-mode emissions test measurements on a sample of major roads in Jabodetabek metropolitan area (Jabodetabek MA hereafter). Road traffic in Jabodetabek MA is characterized with high congestion levels during day times. Thus, there is great number of vehicles operating at idle or stop-and-go driving conditions. It is known that vehicle exhaust emissions of NOx, CO, HC and particles are different at driving conditions, being the highest during acceleration (Pujadas, M et al., 2004). Nevertheless, we can assume that emission test results can be used to represent the real world conditions at sites over-congested traffic that result in long idling times. Equally, CO emissions detected at idle mode test can be used to represent intersections where CO hotspots occur depending on traffic volume and signal timing as that emission rate at idle mode is the same as for driving at 2.5 mph (Pujadas, M et al., 2004). By using the data collected from the sampled vehicles drawn randomly at several major roads in Jabodetabek, we can compare the actual emission conditions of in-use vehicle with the Jakarta’s standards. The Bivariate Probit analysis is conducted for the likelihood of carbon monoxide and hydrocarbon emission violations given a set of vehicle characteristics. This methodology finds the effects of characteristics such as age, engine size, air-fuel ratio, carburetors, fuel type and non-sedan car on the likelihood of emissions test failure. Preliminary assessment results from this study can be used as an initial review of the


1378

implementation of a new regulation. More importantly, the results can also be used as a groundwork for selective sampling process which might substantially improve the cost-effectiveness of the I/M program in Jakarta city. 2. METHODOLOGY 2.1 Inspection and Maintenance Program Vehicle emission control program aims to reduce emissions to a level necessary to maintain environmental quality at desired levels. To address emissions from mobile sources, an integrated approach is needed, because some of the measures can only be effective if they are enforced along with other measures. Tightening new vehicle emission standards should be accompanied by concomitant tightening of in-use standards for those newer model vehicles. These standards form the basis for routine vehicle emission inspection carried out as part of the inspection and maintenance (I/M) program. Related to the engine and emission control devices, the maintenance is an important factor. Vehicle emission control systems begin to function improperly while the vehicles are still being driven. Combustion-powered vehicles naturally tend to deteriorate with age and usage. Thus, old vehicles have become a major problem in many metropolitan areas. Also, minor malfunctions in the air/fuel or spark management system can increase emissions significantly. For all these problems, it is important to maintain a vehicle regularly for detecting malfunctions giving rise to emissions (ADB, 2003). As well, emissions level is also influenced by the driving cycles. As cars in heavy traffic have to stop and go repeatedly, the emission levels are clearly higher than the cars in free flow conditions. Besides fuel type and its quality affect level of emissions Implementation of in-use vehicle emission standards and also vehicles emission testing does not usually result in additional direct costs for governments. Usually, implementation costs transferred to vehicle owners. In case of Jakarta city, the local government endorse the private vehicle mechanic workshops to perform the I/M program. The private vehicles owner can do the emission test and vehicles maintenance in order to meet the allowable standard. Considering the private cars populations in Jakarta city, the capability of private mechanic workshops to perform the I/M program is one of the key issues. Therefore, output of this study can also be used as significant input to the policy makers to identify the characteristic that are most likely to signify that vehicles are high polluters. 2.2 Data Analysis In this study, we use the bivariate probit regression analysis to examine the likelihood of CO and HC emission violations. This study only performs an analysis of gasoline vehicles. Table 1 provides vehicles emission standards of CO and HC for Jakarta City, as stated in The Governor Decree No 95 Year 2000. In the model, emission test violation is defined as the effect of vehicle characteristics such as vehicle type, model year, engine size capacity, carburetor, and injection systems. Other factors used in the analysis are considered to be of operational nature such as fuel type used by cars and air-fuel mixture ratio. Using all independent variables, we propose a bivariate binary Probit regression model of the emission test failure. Bivariate binary probit regression model depends on simultaneous observation of two discrete binary observed-dependent variables, i.e., yi1 and yi2, that indicates the emissions


1379

test failures of CO and HC. Based on the observed dependent variables that take binary discrete values, underlying continuous dependent variables, zi1 and zi2, can be expressed as:

22

11

ii

ii

z

z

ε

ε

+′=

+′=

i22

i11

xβ

xβ, yij = 1 if zij > 0, yij = 0 otherwise, j = {1, 2} (1)

where i denotes an observation; β and x stand for the vectors of parameters and the independent variables respectively; εi1 and εi2 are random variates distributed jointly as standard Bivariate Normal and a free correlation parameter, ρ, i.e., BNV [0,0,1,1, ρ]. Based on the equation given above, the log- likelihood function of the sample can be given as:

[ ]∑ ′′Φ=i

iiii qqqqL ρ2122212 ,,loglog ii11 xβxβ (2)

where Ф2 stands for the standard Bivariate Normal distribution; q is an indicator variable such that qim = 2yim-1, m = {1, 2}. Based on data collected by the on-road measurement at nine major roads in Jabodetabek in 2004, the model is estimated by using LIMDEP Version 8.0 econometric software (Greene 2002).

Table 1 Jakarta’ in-use gasoline vehicles emission standards Model year CO (%) HC (ppm) Carburetor car Pre-1985

1986-1995 1996 and newer

4.0 3.5 3.0

1000 800 700

Injection car 1986-1995 1996 and newer

3.0 2.5

600 500

Source: Governor Decree of Jakarta No 95/2000

In the model, the vehicle age determined by using model year of vehicle subtracted from 2005. According to vehicle age distribution, we classify total samples into ten groups with an interval of three years. Based on the distribution of engine size, we created five classes of the engine size capacity (Table 2). For vehicle type, we use sedan and non-sedan classification. Lastly the fuel types are categorized into regular or premium and non-regular which in Jakarta’s fuel market call as pertamax or pertamax plus.

3. DATA Nine locations were selected as the onsite idle emission measurement spots (check points). Five of them were located in Jakarta city; the remaining four spots fall in the Bodetabek region (Figure 1). The data contain information on the various vehicle characteristics of tested vehicles, such as vehicle registration number, manufacturer, make and model as well as model year, engine size, the carburetor, injection system and fuel type.


1380

Figure 1 Locations of on-road emission measurements of passenger cars The idle-emissions tests were performed at hot start stationary conditions. A single test takes a few minutes: first, the vehicle is reduced to neutral condition, and then to idling condition for at least 30 seconds. Afterwards, the engine is operated at 2500 rotations-per-minute for at least 30 seconds and then brought to idling condition for another 30 seconds. Based on electrochemical cells, an Optima 4040 or SAGEM portable monitor have been used in measuring emissions levels. Emissions levels of CO and HC are detected directly from the output signals of the monitor used. Total sample size obtained from on-site emissions measurements is 787 vehicles. Descriptive statistics of the variables used in the regression analysis are presented in Table 2 and Table 3. As regards vehicle characteristics, about 76.49 % of all vehicles are non-sedan cars, and about 64.55 % of all are carburetor cars. About 81.45% of vehicles use regular gasoline and the average air-to-fuel ratio is about 1.0067. The vehicles tested are more than 51% below or equal to six years old. The engine size capacity of vehicles concentrated in the range between 1000 cc to 2000 cc respectively (more than 88%). Two dependent variables for regression analysis are related to the emissions measurements of CO and HC. The dependent variables indicate emissions test failures of a vehicle, i.e., the vehicle emissions exceeds the levels designated in the standards. Table 2 shows that 418 vehicles have both CO and HC emissions below standards; 348 vehicles that have CO emissions above the standard and 90 vehicles have HC emissions above the standard. The odds ratio of CO and HC emissions test failures are about 6 and 3 times the opposite cases. Inspecting Figure 2, one can notice that vehicle age positively affects CO emission test failure. In Figure 3, one can see that the relationship between engine size and CO emission test failure forms a U-shape: CO emission test failure reduces with the engine size up to 2500cc, and then rises. The actual rates show that the vehicles with engine sizes between 2000cc and 2500 cc pose the lowest probability for emissions test failures. This figure indicates that the emissions control devices are functions best in medium sized vehicle engines. The actual probability for emissions failure by engine size capacity is consistent with previous analysis results of

5, Jakarta

1, Bogor

1, Bekasi 1, Tangerang

1, Depok


1381

vehicle emissions (Bin, O, 2003). However, we don not observe a similar pattern for HC emission test failure. Thus, we conclude that most of the emission test failure of vehicles caused by the CO emission test failure.

Table 2 Variable definitions of Vehicle Characteristics Definitions Results of emission test Variables

Failure of CO

Failure of HC

Failure both

Failure either

Whole samples

Non-sedan =1, if non-sedan; =0, otherwise

48.8% 13.5% 11.0% 51.2% 76.5%

FCARB =1, if carburetor; =0, otherwise

54.1% 13.6% 9.8% 57.9% 64.6%

Fuel =1, if regular fuel; =0, otherwise

48.2% 13.0% 9.7% 51.5% 81.5%

ENGSCL: engine size class by cubic centimeter displacement

(1) -1000cc (2) 1000-1500cc (3) 1500-2000cc (4) 2000-2500cc (5) 2500-

63.6%47.3%40.3%22.6%62.5%

10.9%11.6%11.8%3.2%

25.0%

9.1%8.0%9.7%0.0%

25.0%

65.5% 50.8% 42.4% 25.8% 62.5%

7.0%39.5%48.5%3.9%1.0%

AGECL: Model year class subtracted from 2005

(1) 1 -3 years (2) 4 -6 years (3) 7 -9 years (4) 10-12 years (5) 13-15 years (6) 16-18 years (7) 19-21 years (8) 22 -24 years (9) 25-27 years (10) 28 years-

34.4%39.9%45.1%53.3%46.3%54.1%51.9%80.0%66.7%50.0%

5.0%11.1%12.1%13.1%14.9%24.3%14.8%0.0%

11.1%33.3%

3.8%9.5%8.8%

11.0%9.0%

16.2%11.1%0.0%

11.1%16.7%

35.6% 41.6% 48.4% 55.5% 52.2% 62.2% 55.6% 80.0% 66.7% 66.7%

20.3%30.9%11.6%17.4%8.5%4.7%3.4%1.3%1.1%0.8%

Table 3 Variable definitions: means and standard deviations of measured parameters

Results of emission test Variables Definitions Failure of

CO Failure of

HC Failure

both Failure either

Whole

samples

No. of samples 348 90 69 369 787 CO Carbon monoxide

measured by the percent of total volume of emission gas

6.086 (2.286)

6.059 (2.286)

7.440 (2.647)

5.826 (2.485)

3.309 (2.987)

HC Hydrocarbon measured by parts per million

539.6 (366.0)

1047.5 (457.0)

1051.3 (504.5)

567.8 (378.1)

390.6 (322.1)

Lambda Air to fuel ratio 0.9041 (0.206)

0.9516 (0.351)

1.0907 (0.287)

0.9144 (0.212)

1.0067 (0.274)


1382

Notes: in each cell, the first row indicates the mean and the second row (inside the brackets) indicates the standard deviations.

Figure 2 Emission test failures of CO and HC by vehicle age

Figure 3 Emission test failures of Co and HC by vehicle engine size

4. MODEL ESTIMATIONS AND RESULTS DISCUSSION


1383

The Bivariate Probit regression analysis is conducted using independent variables of vehicle age, engine size, air-fuel mixture (λ), fuel type, carburetors, and non-sedan type. In order to ascertain the influence of main factors (vehicle age, engine size, non-sedan, lambda) and the additional factors (fuel type and carburetor) on vehicle emissions (i.e., CO and HC), two alternative specifications are estimated to test for the significance of inclusion of the factors other than main factors. The first model Model 1 includes the main factors only; in addition to the main factors, the second model Model 2 uses all factors. Table 4 reports the estimation results of the regression analyses for both Model 1 and Model 2. Comparing the main factors estimation and all factor estimations, the disturbance correlation value is almost same. The log likelihood estimation results for model which incorporate all factors are lower than model of main factors. The coefficients of constant terms for CO are always positive. In the contrary, the coefficients are negative for HC. Non-sedan, vehicle age and lambda are always significant in the determining the emission test failure for both CO and HC at any estimation results. Engine size capacity is significant in the CO emission test failure for both model 1 and 2. In contrast, engine size capacity of the vehicles is insignificant to the emission test failure for HC. By incorporating variable of carburetor into the model, it gives a significant impact only on the CO emission test failure. Fuel used by the cars is also significant to reduce the CO and HC emission level which it will determine the emission test failure.

Table 4 Estimation results of bivariate probit models for passenger car in Jakarta city

Model 1 Model 2 CO HC CO HC Variables

coefficient t-score coefficient t-score coefficient t-score coefficient t-scoreNon-sedan AGECL ENGCL I/M Code Carburetor Fuel Constant

0.587 0.119

-0.234 -2.314

--- ---

1.914

4.47 4.43

-3.33 -18.8

--- --- 6.83

0.583 0.111 0.004

-0.615 --- ---

-1.45

3.30 3.37 0.05

-2.21 --- ---

-3.46

0.329 0.022 -0.066 0.369 0.379 0.238 -1.706

2.67 2.65

-2.00 -17.3 2.97 2.13 4.46

0.481 0.081 0.070 0.280

-0.036 0.406

-3.179

2.67 2.67 0.46

-2.05 0.29 2.03

-3.95 No. of

samples 787 787

Log Likelihood -708.4 -697.4

Rho-square 0.435 0.426

The estimation results of the variable of vehicle age always positive and significant both for CO and HC emission test failure in the model 1 and 2. Looking at the mean of CO and HC concentration emitted from the passenger car which shown in the figure 4, the relationship between vehicle emission (CO and HC) and the vehicle age is linear. We can see that the mean of emission concentration of CO and HC will increase as the function of vehicle age. If we compare the average emission level to the Jakarta’s standard (Table 1), HC emission level from passenger car in Jakarta city is better than CO emission level in order to meet the standard. It is difficult to conclude the real emission conditions of old vehicles (more than 20 years old) which also shown in figure 4 based on the limited samples which tested in this study.


1384

Figure 4 Average emission levels of CO and HC by vehicle age

By incorporating all variables in the all factors in model 2, lambda keep remain stable as one of significant factor at 1% level which influence to the CO and 5 % for HC emission test failure. One key parameter very effective in the formation of CO and HC is the air-to-fuel ratio when the fuel is burned (Rubin, E.S., 2001). In this study, air–to-fuel ratio (lambda) is taken as the stoichiometric amount. When air–to-fuel ratio is less than 1, it is impossible to burn all fuel as there is insufficient air. In this case, CO and HC should be emitted as products of incomplete combustion. Generally, air–to-fuel ratio is subject to vehicle maintenance conditions. Routine maintenance keeps air-to-fuel ratio near the stoichiometric amount, which automatically sustains the vehicle emissions at the desired levels. In case of Jakarta city, for further analysis, we classify the air-to-fuel ratio which measured from vehicles into the following index: very good (0.95≤ λ <1.05), good (0.9≤ λ <0.95 & 1.05≤ λ <1.1), moderate (0.85≤ λ <0.9 & 1.1≤ λ <1.15), bad (0.8≤ λ <0.85 & 1.15≤ λ <1.2) and very bad (λ <0.8 & 1.2≤ λ) as can be seen in Table 5. Based on the data, we can observe that the relationship between maintenance quality and vehicle emission level of CO and HC is linear (Figure 5). The CO and HC emission level will increase as the function of decreasing of the vehicle maintenance quality which represent as the value of air-to-fuel ratio (λ). In case of urban air quality management, the maintenance of vehicle is the owner responsibility.

Table 5 Definition of air-to-fuel ratio (Lambda) as stoichiometric amount

I/M quality Air to fuel ratio (lambda) No. of samples (%) Very good 0.95≦λ＜1.05 275 ( 34.9%)Good 0.90≦λ＜0.95, 1.05≦λ＜1.10 168 ( 21.3%)Moderate 0.85≦λ＜0.90, 1.10≦λ＜1.15 132 ( 16.8%) Bad 0.80≦λ＜0.85, 1.15≦λ＜1.20 99 ( 12.6%)Very bad λ＜0.80, 1.20≦λ 113 ( 14.4%)

Total 787 (100.0%)


1385

Figure 5 Vehicle emission levels of CO and HC by maintenance quality

The other factor which always significant in the determining of the probability of emission test failure is fuel used by cars. In this study, we classify into two groups which is regular fuel user and non-regular user. In the case of Jakarta city, for non-regular user, might be use high quality fuel which consist of two kinds of gasoline which call as pertamax and pertamax plus. By using the non-regular gasoline which has higher octane number compare to regular gasoline, the engine performance will increase and it will decrease the emission level of both CO and HC. In the context of urban air quality management, government was responsible to determine the fuel quality which sold to the passenger car user. 5. CONCLUSIONS AND FUTURE RESEACH ISSUES The results obtained in this study by using bivariate probit regression model on I/M vehicles emission checking at several major roads in Jakarta city has successfully identified the characteristic of vehicles that are significantly associated with emission test which consist of Carbon Monoxide and Hydrocarbon emission test failure. As a preliminary assessment of the new program on I/M in Jakarta city, this study indicates that Non-sedan type of car, vehicle age, and air-to-fuel ratio, carburetor type of equipment used to supply the fuel in the engine system and fuel that used by cars all play a significant role in determining I/M test results. Engine size capacity take part significantly in the determining the CO emission test failure and become insignificant for HC emission test failure. Information from this study can be used as the initial review results based on the actual conditions at the several major roads in Jakarta and its neighborhood. In the context of urban air quality management and the social capacity, we can further classify the actor’s responsibility in order to reduce the emission from passenger car. The government should be take part into the variables of fuel quality and vehicle age. The industry (car producers) should be responsible with the emission control technology and


1386

equipment which is lower for non-sedan car compare to sedan car. Government also can take part in the determining the minimum criteria of emission control equipment which should be installed for non-sedan car. Citizen or car user should be fully responsible to do regular and routine vehicle maintenance and repair in order to reduce their car emissions. In the beginning of the launching I/M program in Jakarta city, the local government mention about the selective car which allowed register in Jakarta city based on their age or banned the old vehicle register in Jakarta city. This situations increase the skepticism of I/M program especially from the vehicle owners. By the study we can conclude that, the vehicle age is significant to determine the emission level. On the other hand, emission level of vehicle also influenced by some other factor which were car type, fuel used and maintenance quality. And so, we should perform further studies in the future which should explore and explain more detail on the relationship between vehicle age and maintenance quality especially focusing on the probability of emission test failure for the same vehicle age category but different maintenance quality. Based on some academic research paper on the factors which influenced to the vehicle emission level, we can support the local government in order to reduce the mobile emission source especially from passenger car in Jakarta city.

ACKNOWLEDGEMENTS

Data was provided by the Local Environmental Management Agency (BPLHD) of DKI Jakarta. This research is partially supported by The 21st Century Center of Excellence Program “Social Capacity Development for Environmental Management and International Development” at Graduate School of International Development and Cooperation, Hiroshima University, Japan.

REFERENCES

Japan International Cooperation Agency (JICA) (1997). The Study on the Integrated Air Quality Management for Jakarta Metropolitan Area, Final Report collaborated with the Environmental Impact Management Agency (BAPEDAL) the Republic of Indonesia, Nippon Koei Co., Ltd., and Suuri Keikaku Co., Ltd.

SEI, UNEP, KEI (2002). Benchmarking Urban Air Quality Management and Practice in Major and Mega Cities of Asia, Stage 1.

Adianto P. Simamora, (2006). “Garages ask for speedier emission testing approval.” The Jakarta Post-The Journal of Indonesia Today, City News. http://www.thejakartapost.com May 01 2006

Badan Pengelolaan Lingkungan Hidup Daerah, Propinsi DKI Jakarta, Kumpulan Peraturan Tentang Pengendalian Pencemaran Udara di Propinsi DKI Jakarta, BPLHD, 2005.

Hubbard, T, (1997). Using Inspection and Maintenance program to regulate vehicle emissions. Contemporary Economic Policy, 15, 52-62.

Washburn, S., Seet, J., Mannering, F. (2001). Statistical Modeling of Vehicle Emission from Inspection/Maintenance testing data: an Explanatory analysis. Transportation Research, Part D, 6, 21-36

Bin, O. (2003) A logit Analysis of vehicles emissions using inspection and maintenance testing data. Transportation Research, Part D, 8, 215-227.


1387

M. Pujadas, Nunez, L., Plaza, J., Bezares, J.C., Fernandez, J.M. (2004) Comparison between experimental and calculated vehicle idle emission factors for Madrid fleet. Science of the Total Environment, 334-335, 134-140

ADB (2003), Reducing Vehicle Emissions in Asia, Asian Development Bank. Manila. Greene, W. (2002). Econometric Analysis, Prentice-Hall, New Jersey. Beydoun, M. and Jean, M.G. (2006) Vehicle Characteristic and Emissions: Logit and

Regression analysis of I/M data from Massachusetts, Maryland and Illinois. Transportation Research, Part D, 11, 59-76

Rubin, E.S. (2001) Introduction to Engineering and the Environment. McGraw-Hill International Edition, Singapore.


1388

Date post:	16-Oct-2021
Category:	Documents
Upload:	others
View:	4 times
Download:	0 times

BIVARIATE PROBIT MODEL OF ON-ROAD EMISSION …

Documents