| Autor: |
Marpaung, T. J., Siringoringo, Yan Batara Putra, Tarigan, Enita Dewi |
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3029 Issue 1, p1-10, 10p |
| Abstrakt: |
The relationship of a variable with other variables can be known by various methods. The statistical method commonly used to see the relationship between two variables is correlation analysis. Correlation analysis is used to see the closeness of the linear relationship between the two variables which is expressed by the correlation coefficient. Correlation analysis that is often used is Pearson's correlation, this correlation is good to use if it fulfills the assumption that the data is normally distributed. The impact of this relationship pattern will be difficult to detect even though there is a fairly close relationship between variables. One method that can be used to determine the relationship between variables that are not normally distributed is the Copula approach. This method is flexible because it does not require data normality assumptions and can combine several marginal distributions into one combined distribution. Copula has the ability to describe the dependency structure between variables with different marginals and model those dependencies. Copula can also clearly describe dependence at the extremes. Dynamic and automatic traffic light settings to set the optimal traffic light cycle according to traffic conditions. The use of Monte Carlo simulation in traffic modeling is also used in the problem of setting single intersections. In this study, a simulation of traffic light settings was carried out using the Monte Carlo method with a case study in the Medan area. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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