Abstract
The process of modeling a random process requires a careful analysis and a correct interpretation of the behavior of the process. In different contexts, different statistical distributions may be eligible for the same model obtained in the study. In response to this situation created quite often in practice, we make use of statistical analysis methods to make possible comparison and decision making regarding the selection of the most appropriate model. In our study the usage of such methods is illustrated by comparing two of models commonly mentioned in literature when it comes to bus headway times modeling. Models under consideration are Gaussian model and Poisson model. To evaluate the performance of these models visual and analytical methods are used in this study. The simulation of these processes is made possible using the power of R language. Although both models have their practicability in a certain degree, tests showed that the Gaussian model fits best with the real model
Highlights
Data collection through direct observations at the bus station was performed during peak hour 7:00 to 9:00
Evaluation of parameters is a very common procedure in statistics when it comes to the problem of finding the model that matches the real process
When we have a population that we believe comes from a specific distribution, we must find the values of the parameters for this distribution in order to present the data in a correct way (Leka, 2004)
Summary
Data collection through direct observations at the bus station was performed during peak hour 7:00 to 9:00. Evaluation of parameters is a very common procedure in statistics when it comes to the problem of finding the model that matches the real process. Bus headways are a very important concept in the theory of urban transport modeling.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
