The objective of this research is to study factors that effect on the CO2 vehicles emissions on Egyptian roads. The models were calibrated using vehicles emission records collected during the study for the period (November 2017). Data recorded for eight vehicles, emission data were classified according to the fuel type to three categories (Diesel, Natural Gas and Petrol Vehicles), and to conduct a comparative analysis of various statistical modeling techniques generalized linear regression models were used such as "Linear Regression with Link Function of Identity, Linear Regression. with Link Function of Log, Gamma Regression with Link Function of Log and Tweedy Regression with Link Function of Log " to predict vehicle emission rates as a function of the independent variables.
Vehicles emission measurements CO2 (g/s) used in this study were obtained from Egyptian Environmental Affairs Agency (EEAA) recorded for the period (November 2017), Seven independent variables were selected in this research (vehicle speed, angle between horizontal alignments, profile grade, ambient temperature, ambient pressure, ambient relative humidity and numbers of rotation per minute for vehicle engine) which affect directly on the vehicle emissions for the different vehicles categories then a comparison of these results obtained from the (SPSS) mathematical model.
Finally, it was found that Linear regression model with link function of log was the best generalized regression model to represent the correlation between CO2 emission for Diesel vehicles, Natural Gas and Petrol vehicles emission.
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